This is part of your
ADVANCED ALGORITHMS IN
COMPUTATIONAL BIOLOGY (C3),
where I will cover the first two weeks’ courses
• 2012/02/24: DATABASES: AN OVERVIEW
• 2012/03/02: INTRODUCTION TO DATA MINING
1
Class Info
• Lecturer: Chi-Yao Tseng (曾祺堯)
cytseng@citi.sinica.edu.tw
• Grading:
– No assignments
– Midterm:
• 2012/04/20
• I’m in charge of 17x2 points out of 120
• No take-home questions
2
Outline
• Introduction
– From data warehousing to data mining
• Mining Capabilities
– Association rules
– Classification
– Clustering
• More about Data Mining
3
Main Reference
• Jiawei Han, Micheline Kamber, Data Mining:
Concepts and Techniques, 2nd Edition, Morgan
Kaufmann, 2006.
– Official website:
http://www.cs.uiuc.edu/homes/hanj/bk2/
4
Why Data Mining?
•
The Explosive Growth of Data: from terabytes to petabytes (1015 B= 1 million GB)
– Data collection and data availability
• Automated data collection tools, database systems, Web,
computerized society
– Major sources of abundant data
• Business: Web, e-commerce, transactions, stocks, …
• Science: Remote sensing, bioinformatics, scientific simulation, …
• Society and everyone: news, digital cameras, YouTube, Facebook
• We are drowning in data, but starving for knowledge!
• “Necessity is the mother of invention”—Data mining—
Automated analysis of massive data sets
5
Why Not Traditional Data Analysis?
• Tremendous amount of data
– Algorithms must be highly scalable to handle such as terabytes of data
• High-dimensionality of data
– Micro-array may have tens of thousands of dimensions
• High complexity of data
• New and sophisticated applications
6
Evolution of Database Technology
•
1960s:
– Data collection, database creation, IMS and network DBMS
•
1970s:
– Relational data model, relational DBMS implementation
•
1980s:
– RDBMS, advanced data models (extended-relational, OO, deductive, etc.)
– Application-oriented DBMS (spatial, scientific, engineering, etc.)
•
1990s:
– Data mining, data warehousing, multimedia databases, and Web databases
•
2000s
– Stream data management and mining
– Data mining and its applications
– Web technology (XML, data integration) and global information systems
7
What is Data Mining?
• Knowledge discovery in databases
– Extraction of interesting (non-trivial, implicit,
previously unknown and potentially useful)
patterns or knowledge from huge amount of data.
• Alternative names:
– Knowledge discovery (mining) in databases (KDD),
knowledge extraction, data/pattern analysis, data
archeology, data dredging, information harvesting,
business intelligence, etc.
8
Data Mining: On What Kinds of Data?
•
Database-oriented data sets and applications
– Relational database, data warehouse, transactional database
•
Advanced data sets and advanced applications
– Data streams and sensor data
– Time-series data, temporal data, sequence data (incl. bio-sequences)
– Structure data, graphs, social networks and multi-linked data
– Object-relational databases
– Heterogeneous databases and legacy databases
– Spatial data and spatiotemporal data
– Multimedia database
– Text databases
– The World-Wide Web
9
Knowledge Discovery (KDD) Process
Interpretation /
Evaluation
Knowledge!
Data Mining
Patterns
Selection &
Transformation
Transformed
data
Data Cleaning
& Integration
Data warehouse
Databases
• This is a view from typical database systems
and data warehousing communities.
• Data mining plays an essential role in the
knowledge discovery process.
10
Data Mining and Business Intelligence
Increasing potential
to support
business decisions
End User
Decision
Making
Data Presentation
Visualization Techniques
Business
Analyst
Data Mining
Information Discovery
Data
Analyst
Data Exploration
Statistical Summary, Querying, and Reporting
Data Preprocessing/Integration, Data Warehouses
Data Sources
Paper, Files, Web documents, Scientific experiments, Database Systems
DBA
11
Data Mining: Confluence of Multiple Disciplines
Database
technology
Applications
Data
visualization
Statistics
Data
Mining
Highperformance
computing
Machine
learning
Pattern
recognition
Algorithms
12
Typical Data Mining System
Graphical User Interface
Pattern Evaluation
Knowledge
Base
Data Mining Engine
Database or Data
Warehouse Server
data cleaning, integration, and selection
Database
Data
warehouse
World-Wide
Web
Other info.
repositories
13
Data Warehousing
• A data warehouse is a subject-oriented,
integrated, time-variant, and nonvolatile
collection of data in support of managements’
decision making process. —W. H. Inmon
14
Data Warehousing
• Subject-oriented:
– Provide a simple and concise view around particular subject issues by
excluding data that are not useful in the decision support process.
• Integrated:
– Constructed by integrating multiple, heterogeneous data sources.
• Time-variant:
– Provide information from a historical perspective (e.g., past 5-10 years.)
• Nonvolatile:
– Operational update of data does not occur in the data warehouse
environment
– Usually requires only two operations: load data & access data.
15
Data Warehousing
• The process of constructing and using data warehouses
• A decision support database that is maintained
separately from the organization’s operational
database
• Support information processing by providing a solid
platform of consolidated, historical data for analysis
• Set up stages for effective data mining
16
Illustration of Data Warehousing
client
Data source in Taipei
Data source in New York
.
.
.
Clean
Transform
Integrate
Load
Data
Warehouse
Query and
Analysis
Tools
client
Data source in London
17
OLTP vs. OLAP
OLTP(On-line Transaction Processing)
Short online transactions:
update, insert, delete
Online-Transaction
Processing
current & detailed data,
Versatile
Tx.
database
Complex Queries
Analytics
Data Mining
Decision Making
OLAP(On-line Analytical Processing)
Data Warehouse
aggregated & historical data,
Static and Low volume
18
Multi-Dimensional View of Data Mining
•
Data to be mined
– Relational, data warehouse, transactional, stream, object-oriented/relational,
active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW
•
Knowledge to be mined
– Characterization, discrimination, association, classification, clustering,
trend/deviation, outlier analysis, etc.
– Multiple/integrated functions and mining at multiple levels
•
Techniques utilized
– Database-oriented, data warehouse (OLAP), machine learning, statistics,
visualization, etc.
•
Applications adapted
– Retail, telecommunication, banking, fraud analysis, bio-data mining, stock
market analysis, text mining, Web mining, etc.
19
Mining Capabilities (1/4)
• Multi-dimensional concept description:
Characterization and discrimination
– Generalize, summarize, and contrast data
characteristics, e.g., dry vs. wet regions
• Frequent patterns (or frequent itemsets),
association
– Diaper  Beer [0.5%, 75%] (support, confidence)
20
Mining Capabilities (2/4)
• Classification and prediction
– Construct models (functions) that describe and distinguish classes or
concepts for future prediction
• E.g., classify countries based on (climate), or classify cars based on
(gas mileage)
– Predict some unknown or missing numerical values
21
Mining Capabilities (3/4)
• Clustering
– Class label is unknown: Group data to form new categories
(i.e., clusters), e.g., cluster houses to find distribution
patterns
– Maximizing intra-class similarity & minimizing interclass
similarity
• Outlier analysis
– Outlier: Data object that does not comply with the general
behavior of the data
– Noise or exception? Useful in fraud detection, rare events
analysis
22
Mining Capabilities (4/4)
• Time and ordering, trend and evolution
analysis
– Trend and deviation: e.g., regression analysis
– Sequential pattern mining: e.g., digital camera 
large SD memory
– Periodicity analysis
– Motifs and biological sequence analysis
• Approximate and consecutive motifs
– Similarity-based analysis
23
More Advanced Mining Techniques
•
Data stream mining
– Mining data that is ordered, time-varying, potentially infinite.
•
•
•
Graph mining
– Finding frequent subgraphs (e.g., chemical compounds), trees (XML),
substructures (web fragments)
Information network analysis
– Social networks: actors (objects, nodes) and relationships (edges)
• e.g., author networks in CS, terrorist networks
– Multiple heterogeneous networks
• A person could be multiple information networks: friends, family,
classmates, …
– Links carry a lot of semantic information: Link mining
Web mining
– Web is a big information network: from PageRank to Google
– Analysis of Web information networks
• Web community discovery, opinion mining, usage mining, …
24
Challenges for Data Mining
•
•
•
•
•
•
•
Handling of different types of data
Efficiency and scalability of mining algorithms
Usefulness and certainty of mining results
Expression of various kinds of mining results
Interactive mining at multiple abstraction levels
Mining information from different source of data
Protection of privacy and data security
25
Brief Summary
• Data mining: Discovering interesting patterns and knowledge from
massive amount of data
• A natural evolution of database technology, in great demand, with wide
applications
• A KDD process includes data cleaning, data integration, data selection,
transformation, data mining, pattern evaluation, and knowledge
presentation
• Mining can be performed in a variety of data
• Data mining functionalities: characterization, discrimination, association,
classification, clustering, outlier and trend analysis, etc.
26
A Brief History of Data Mining Society
•
1989 IJCAI Workshop on Knowledge Discovery in Databases
– Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991)
•
1991-1994 Workshops on Knowledge Discovery in Databases
– Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. PiatetskyShapiro, P. Smyth, and R. Uthurusamy, 1996)
•
1995-1998 International Conferences on Knowledge Discovery in Databases and
Data Mining (KDD’95-98)
– Journal of Data Mining and Knowledge Discovery (1997)
•
ACM SIGKDD conferences since 1998 and SIGKDD Explorations
•
More conferences on data mining
– PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), etc.
•
ACM Transactions on KDD starting in 2007
More details here: http://www.kdnuggets.com/gpspubs/sigkdd-explorations-kdd-10-years.html
27
Conferences and Journals on Data Mining
• KDD Conferences
– ACM SIGKDD Int. Conf. on
Knowledge Discovery in
Databases and Data Mining
(KDD)
– SIAM Data Mining Conf. (SDM)
– (IEEE) Int. Conf. on Data Mining
(ICDM)
– European Conf. on Machine
Learning and Principles and
practices of Knowledge Discovery
and Data Mining (ECML-PKDD)
– Pacific-Asia Conf. on Knowledge
Discovery and Data Mining
(PAKDD)
– Int. Conf. on Web Search and
Data Mining (WSDM)
• Other related conferences
– DB: ACM SIGMOD, VLDB,
ICDE, EDBT, ICDT
– WEB & IR: CIKM, WWW, SIGIR
– ML & PR: ICML, CVPR, NIPS
• Journals
– Data Mining and Knowledge
Discovery (DAMI or DMKD)
– IEEE Trans. On Knowledge and
Data Eng. (TKDE)
– KDD Explorations
– ACM Trans. on KDD
28
CAPABILITIES OF DATA MINING
29
FREQUENT PATTERNS &
ASSOCIATION RULES
30
Basic Concepts
• Frequent pattern: a pattern (a set of items, subsequences,
substructures, etc.) that occurs frequently in a data set
• First proposed by Agrawal, Imielinski, and Swami [AIS93] in the
context of frequent itemsets and association rule mining
• Motivation: Finding inherent regularities in data
– What products were often purchased together?— Beer and diapers?!
– What are the subsequent purchases after buying a PC?
– What kinds of DNA are sensitive to this new drug?
– Can we automatically classify web documents?
• Applications
– Basket data analysis, cross-marketing, catalog design, sale campaign
analysis, Web log (click stream) analysis, and DNA sequence analysis
31
Mining Association Rules
• Transaction data analysis. Given:
– A database of transactions (Each tx. has a list of
items purchased)
– Minimum confidence and minimum support
• Find all association rules: the presence of one
set of items implies the presence of another
set of items
Diaper  Beer [0.5%, 75%]
(support, confidence)
32
Two Parameters
• Confidence (how true)
– The rule X&YZ has 90% confidence:
means 90% of customers who bought X and Y also
bought Z.
• Support (how useful is the rule)
– Useful rules should have some minimum
transaction support.
33
Mining Strong Association Rules in
Transaction Databases (1/2)
• Measurement of rule strength in a transaction
database.
AB [support, confidence]
support  Pr( A  B) 
# of tx containing all items in A  B
total # of tx
# of tx containingboth A  B
confidence Pr(B | A) 
# of tx containingA
34
Mining Strong Association Rules in
Transaction Databases (2/2)
• We are often interested in only strong
associations, i.e.,
– support  min_sup
– confidence  min_conf
• Examples:
– milk  bread [5%, 60%]
– tire and auto_accessories  auto_services [2%,
80%].
35
Example of Association Rules

Transaction-id
Items bought
1
A, B, D
2
A, C, D
3
A, D, E
4
B, E, F
5
B, C, D, E, F
Let min. support = 50%, min. confidence = 50%
 Frequent patterns: {A:3, B:3, D:3, E:3, AD:3}
 Association rules: A  D (s = 60%, c = 100%)
D  A (s = 60%, c = 75%)
36
Two Steps for Mining Association Rules
• Determining “large (frequent) itemsets”
– The main factor for overall performance
– The downward closure property of frequent
patterns
• Any subset of a frequent itemset must be frequent
• If {beer, diaper, nuts} is frequent, so is {beer, diaper}
• i.e., every transaction having {beer, diaper, nuts} also
contains {beer, diaper}
• Generating rules
37
The Apriori Algorithm
• Apriori (R. Agrawal and R. Srikant. Fast algorithms
for mining association rules. VLDB'94.)
– Derivation of large 1-itemsets L1: At the first
iteration, scan all the transactions and count the
number of occurrences for each item.
– Level-wise derivation: At the kth iteration, the
candidate set Ck are those whose every (k-1)-item
subset is in Lk-1. Scan DB and count the # of
occurrences for each candidate itemset.
38
The Apriori Algorithm—An Example
min. support =2 tx’s (50%)
Database TDB
Tid
Items
100
A, C, D
200
B, C, E
300
A, B, C, E
400
B, E
L2
Itemset
{A, C}
{B, C}
{B, E}
{C, E}
C3
C1
1st scan
sup
2
2
3
2
Itemset
{B, C, E}
C2
Itemset
sup
{A}
Itemset
sup
2
{A}
2
{B}
3
{B}
3
{C}
3
{C}
3
{D}
1
{E}
3
{E}
3
Itemset
{A, B}
{A, C}
{A, E}
{B, C}
{B, E}
{C, E}
3rd scan
L1
sup
1
2
1
2
3
2
L3
C2
2nd scan
Itemset
{A, B}
{A, C}
{A, E}
{B, C}
{B, E}
{C, E}
Itemset
sup
{B, C, E}
2
39
From Large Itemsets to Rules
• For each large itemset m
– For each subset p of m
if ( sup(m) / sup(m-p)  min_conf )
• output the rule (m-p)p
– conf. = sup(m)/sup(m-p)
– support = sup(m)
•
m = {a,c,d,e,f,g} 2000 tx’s,
p = {c,e,f,g}
m-p = {a,d} 5000 tx’s
– conf. = # {a,c,d,e,f,g} / # {a,d}
– rule: {a,d} {c,e,f,g}
confidence: 40%, support: 2000 tx’s
40
Redundant Rules
• For the same support and confidence, if we
have a rule {a,d} {c,e,f,g}, do we have
[agga98a]:
– {a,d} {c,e,f} ?
– {a} {c,e,f,g} ?
– {a,d,c} {e,f,g} ?
– {a} {c,d,e,f,g} ?
Yes!
Yes!
No!
No!
41
Practice
• Suppose we additionally have
– 500 ACE
– 600 BCD
– Support = 3 tx’s (50%), confidence = 66%
• Repeat the large itemset generation
– Identify all large itemsets
• Derive up to 4 rules
– Generate rules from the large itemsets with the
biggest number of elements (from big to small)
42
Discussion of The Apriori Algorithm
• Apriori (R. Agrawal and R. Srikant. Fast algorithms for mining
association rules. VLDB'94.)
– Derivation of large 1-itemsets L1: At the first iteration, scan
all the transactions and count the number of occurrences
for each item.
– Level-wise derivation: At the kth iteration, the candidate
set Ck are those whose every (k-1)-item subset is in Lk-1.
Scan DB and count the # of occurrences for each candidate
itemset.
• The cardinalitiy (number of elements) of C2 is huge.
• The execution time for the first 2 iterations is the
dominating factor to overall performance!
• Database scan is expensive.
43
Improvement of the Apriori Algorithm
• Reduce passes of transaction database scans
• Shrink the number of candidates
• Facilitate the support counting of candidates
44
Example Improvement 1- Partition:
Scan Database Only Twice
• Any itemset that is potentially frequent in DB
must be frequent in at least one of the
partitions of DB
– Scan 1: partition database and find local frequent
patterns
– Scan 2: consolidate global frequent patterns
• A. Savasere, E. Omiecinski, and S. Navathe. An efficient
algorithm for mining association in large databases. In
VLDB’95
45
Example Improvement 2- DHP
• DHP (direct hashing with pruning): Apriori + hashing
– Use hash-based method to reduce the size of C2.
– Allow effective reduction on tx database size (tx number and
each tx size.)
Tid
Items
100
A, C, D
200
B, C, E
300
A, B, C, E
400
B, E
J. Park, M.-S. Chen, and P. Yu.
An effective hash-based algorithm for mining association rules. In SIGMOD’95.
46
Mining Frequent Patterns w/o
Candidate Generation
• A highly compact data structure: frequent
pattern tree.
• An FP-tree-based pattern fragment growth
mining method.
• Search technique in mining: partitioningbased, divide-and-conquer method.
• J. Han, J. Pei, Y. Yin, Mining Frequent Patterns without
Candidate Generation, in SIGMOD’2000.
47
Frequent Patter Tree (FP-tree)
• 3 parts:
– One root labeled as ‘null’
– A set of item prefix subtrees
– Frequent item header table
• Each node in the prefix subtree consists of
– Item name
– Count
– Node-link
• Each entry in the frequent-item header table consists of
– Item-name
– Head of node-link
48
The FP-tree Structure
frequent item header table
Item
f
c
a
b
m
p
root
Head of node-links
f:4
c:1
c:3
b:1
b:1
a:3
p:1
m:2
b:1
p:2
m:1
49
FP-tree Construction: Step1
• Scan the transaction database DB once (the
first time), and derives a list of frequent items.
• Sort frequent items in frequency descending
order.
• This ordering is important since each path of a
tree will follow this order.
50
Example (min. support = 3)
Tx ID
Items Bought
(ordered) Frequent Items
100
f,a,c,d,g,i,m,p
f,c,a,m,p
200
a,b,c,f,l,m,o
f,c,a,b,m
300
b,f,h,j,o
f,b
400
b,c,k,s,p
c,b,p
500
a,f,c,e,l,p,m,n
f,c,a,m,p
List of frequent items:
(f:4), (c:4), (a:3), (b:3), (m:3), (p:3)
frequent item header table
Item
Head of node-links
f
c
a
b
m
p
51
FP-tree Construction: Step 2
• Create a root of a tree, label with “null”
• Scan the database the second time. The scan of the first tx
leads to the construction of the first branch of the tree.
Scan of 1st transaction: f,a,c,d,g,i,m,p
The 1st branch of the tree <(f:1),(c:1),(a:1),(m:1),(p:1)>
root
f:1
c:1
a:1
m:1
p:1
52
FP-tree Construction: Step 2 (cont’d)
• Scan of 2nd transaction:
a,b,c,f,l,m,o  f,c,a,b,m
root
f:2
two new nodes:
(b:1) (m:1)
c:2
a:2
m:1
b:1
p:1
m:1
53
The FP-tree
frequent item header table
Item
f
c
a
b
m
p
Tx ID
Items Bought
(ordered) Frequent Items
100
f,a,c,d,g,i,m,p
f,c,a,m,p
200
a,b,c,f,l,m,o
f,c,a,b,m
300
b,f,h,j,o
f,b
400
b,c,k,s,p
c,b,p
500
a,f,c,e,l,p,m,n
f,c,a,m,p
root
Head of node-links
f:4
c:1
c:3
b:1
b:1
a:3
p:1
m:2
b:1
p:2
m:1
54
Mining Process
• Starts from the least frequent item p
– Mining order: p -> m -> b -> a -> c -> f
frequent item header table
Item
Head of node-links
f
c
a
b
m
p
55
Mining Process for item p
• Starts from the least frequent item p
min. support = 3
root
f:4
c:1
Two paths:
<f:4, c:3, a:3, m:2, p:2>
<c:1, b:1,p:1>
c:3
b:1
b:1
a:3
p:1
m:2
b:1
Conditional pattern based of ”p”:
<f:2, c:2, a:2, m:2>
<c:1, b:1>
Conditional frequent pattern:
<c:3>
So we have two frequent patterns:
{p:3}, {cp:3}
p:2
m:1
56
Mining Process for Item m
min. support = 3
root
f:4
c:1
Two paths:
<f:4, c:3, a:3, m:2>
<f:4, c:3, a:3, b:1, m:1>
c:3
b:1
b:1
a:3
p:1
m:2
b:1
Conditional pattern based of ”m”:
<f:2, c:2, a:2>
<f:1, c:1, a:1, b:1>
Conditional frequent pattern:
<f:3, c:3, a:3>
p:2
m:1
57
Mining m’s Conditional FP-tree
Mine (<f:3, c:3, a:3> | m)
a
(am:3)
Mine (<f:3, c:3> | am)
c
f
(cam:3)
Mine (<f:3> | cam)
(fam:3)
c
(cm:3)
Mine (<f:3> | cm)
f
(fm:3)
f
(fcm:3
)
f
(fcam:3)
So we have frequent patterns:
{m:3}, {am:3}, {cm:3}, {fm:3}, {cam:3}, {fam:3}, {fcm:3}, {fcam:3}
58
Analysis of the FP-tree-based method
• Find the complete set of frequent itemsets
• Efficient because
– Works on a reduced set of pattern bases
– Performs mining operations less costly than
generation & test
• Cons:
– No advantages if the length of most tx’s are short
– The size of FP-tree not always fit into main memory
59
Generalized Association Rules
• Given the class hierarchy (taxonomy), one would
like to choose proper data granularities for
mining.
• Different confidence/support may be considered.
• R. Srikant and R. Agrawal, Mining generalized association
rules, VLDB’95.
60
Concept Hierarchy
Clothes
Outerwear
Shirts
Footwear
Shoes
Jackets Ski Pants
Hiking Boots
Freq. itemset
Itemset
support
Jacket
2
Outerwear
3
Clothes
4
Shoes
2
Hiking Boots
2
Footwear
4
Outerwear, Hiking Boots
2
Tx ID
Items Bought
100
Shirt
Clothes, Hiking Boots
2
200
Jacket, Hiking Boots
Outerwear, Footwear
2
300
Ski Pants, Hiking Boots
Clothes, Footwear
2
400
Shoes
500
Shoes
600
Jacket
sup(30%)
conf(60%)
Outerwear -> Hiking Boots
33%
66%
Outerwear -> Footwear
33%
66%
Hiking Boots -> Outerwear
33%
100%
Hiking Boots -> Clothes
33%
100%
Jacket -> Hiking Boots
16%
50%
Ski Pants -> Hiking Boots
16%
100%
61
Generalized Association Rules
reduced support
uniform support
Level 1
min_sup = 5%
Level 2
min_sup = 5%
Level 1
min_sup = 12%
Level 2
min_sup = 3%
Milk
[support = 10%]
2% Milk
[support = 6%]
Skim Milk
[support = 4%]
Milk
[support = 10%]
2% Milk
Not examined
Level 1
min_sup = 5%
Level 2
min_sup = 3%
level filtering
Skim Milk
Not examined
62
Other Relevant Topics
• Max patterns
– R. J. Bayardo. Efficiently mining long patterns from databases.
SIGMOD'98.
• Closed patterns
– N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent
closed itemsets for association rules. ICDT'99.
• Sequential Patterns
– What items one will purchase if he/she has bought some certain items.
– R. Srikant and R. Agrawal, Mining sequential patterns, ICDE’95
• Traversal Patterns
– Mining path traversal patterns in a web environment where
documents or objects are linked together to facilitate interactive
access.
– M.-S. Chen, J. Park and P. Yu. Efficient Data Mining for Path Traversal
Patterns. TKDE’98.
and more…
63
CLASSIFICATION
64
Classification
• Classifying tuples in a database.
• Each tuple has some attributes with known
values.
• In training set E
– Each tuple consists of the same set of multiple
attributes as the tuples in the large database W.
– Additionally, each tuple has a known class identity.
65
Classification (cont’d)
• Derive the classification mechanism from the
training set E, and then use this mechanism to
classify general data (in testing set.)
• A decision tree based approach has been
influential in machine learning studies.
66
Classification –
Step 1: Model Construction
• Train model from the existing data pool
Training
Data
name
age
Classification algorithm
income own cars?
Sandy <=30
low
no
Bill
<=30
low
yes
Fox
31…40 high
yes
Susan >40
med
no
Claire >40
med
no
31…40 high
yes
Andy
Classification rules
67
Classification –
Step 2: Model Usage
Testing
Data
Classification rules
name
age
income own cars?
John
>40
hight
?
Sally
<=30
low
?
No
?
Yes
Annie 31…40 high
No
68
What is Prediction?
• Prediction is similar to classification
– First, construct model
– Second, use model to predict future of unknown
objects
• Prediction is different from classification
– Classification refers to predict categorical class
label.
– Prediction refers to predict continuous values.
• Major method: regression
69
Supervised vs. Unsupervised
Learning
• Supervised learning (e.g., classification)
– Supervision: The training data (observations,
measurements, etc.) are accompanied by labels
indicating the class of the observations.
• Unsupervised learning (e.g., clustering)
– We are given a set of measurements, observations,
etc. with the aim of establishing the existence of
classes or clusters in the data.
– No training data, or the “training data” are not
accompanied by class labels.
70
Evaluating Classification Methods
• Predictive accuracy
• Speed
– Time to construct the model and time to use the model
• Robustness
– Handling noise and missing values
• Scalability
– Efficiency in large databases (not memory resident data)
• Goodness of rules
– Decision tree size
– The compactness of classification rules
71
A Decision-Tree Based Classification
• A decision tree of whether going to play tennis or not:
outlook
sunny
overcast
humidity
high
N
rainy
low
P
P
windy
Yes
N
No
P
• ID-3 and its extended version C4.5 (Quinlan’93):
A top-down decision tree generation algorithm
72
Algorithm for Decision Tree Induction
(1/2)
• Basic algorithm (a greedy algorithm)
– Tree is constructed in a top-down recursive divide-andconquer manner.
– Attributes are categorical.
(if an attribute is a continuous number, it needs to be discretized in
advance.) E.g.
0 <= age <= 100
0 ~ 20
61 ~ 80
21 ~ 40
81 ~ 100
41 ~ 60
– At start, all the training examples are at the root.
– Examples are partitioned recursively based on selected
attributes.
73
Algorithm for Decision Tree Induction
(2/2)
• Basic algorithm (a greedy algorithm)
– Test attributes are selected on the basis of a heuristic or
statistical measure (e.g., information gain): maximizing an
information gain measure, i.e., favoring the partitioning
which makes the majority of examples belong to a single
class.
– Conditions for stopping partitioning:
• All samples for a given node belong to the same class
• There are no remaining attributes for further partitioning –
majority voting is employed for classifying the leaf
• There are no samples left
74
Decision Tree Induction: Training Dataset
age
<=30
<=30
31…40
>40
>40
>40
31…40
<=30
<=30
>40
<=30
31…40
31…40
>40
income student credit_rating
high
no fair
high
no excellent
high
no fair
medium
no fair
low
yes fair
low
yes excellent
low
yes excellent
medium
no fair
low
yes fair
medium
yes fair
medium
yes excellent
medium
no excellent
high
yes fair
medium
no excellent
buys_computer
no
no
yes
yes
yes
no
yes
no
yes
yes
yes
yes
yes
no
75
Age?
<= 30
31…40
> 40
76
Primary Issues in Tree Construction (1/2)
• Split criterion: Goodness function
– Used to select the attribute to be split at a tree
node during the tree generation phase
– Different algorithms may use different goodness
functions:
• Information gain (used in ID3/C4.5)
• Gini index (used in CART)
77
Primary Issues in Tree Construction (2/2)
• Branching scheme:
– Determining the tree branch to which a sample
belongs
Income:
Income:
Income:
high
medium
low
– Binary vs. k-ary splitting
• When to stop the further splitting of a node?
e.g. impurity measure
• Labeling rule: a node is labeled as the class to
which most samples at the node belongs.
78
How to Use a Tree?
• Directly
– Test the attribute value of unknown sample against the
tree.
– A path is traced from root to a leaf which holds the label.
• Indirectly
– Decision tree is converted to classification rules.
– One rule is created for each path from the root to a leaf.
– IF-THEN is easier for humans to understand .
79
Attribute Selection Measure:
Information Gain (ID3/C4.5)



Select the attribute with the highest information gain
Let pi be the probability that an arbitrary tuple in D belongs to
class Ci, estimated by |Ci, D|/|D|
Expected information (entropy) needed to classify a tuple in D:
m
Info( D)   pi log2 ( pi )
i 1

Expected information (entropy):
 Entropy is a measure of how "mixed up" an attribute is.
 It is sometimes equated to the purity or impurity of a variable.
 High Entropy means that we are sampling from a uniform
(boring) distribution.
80
Expected Information (Entropy)

Expected information (entropy) needed to classify a tuple in D:
m
Info( D)   pi log2 ( pi ) (m: number of labels)
i 1
3
3 2
2
Info( D )  I (3,2)   log2 ( )  log2 ( )
5
5 5
5
3
2
   (0.737)   (1.322)
5
5
5
5 0
0
Info( D)  I (5,0)   log2 ( )  log2 ( )
5
5 5
5
 00  0
81
Attribute Selection Measure:
Information Gain (ID3/C4.5)



Select the attribute with the highest information gain
Let pi be the probability that an arbitrary tuple in D belongs to
class Ci, estimated by |Ci, D|/|D|
Expected information (entropy) needed to classify a tuple in D:
m
Info( D)  I ( D)   pi log2 ( pi )
i 1

Information needed (after using A to split D into v partitions) to
v |D |
classify D:
j
InfoA ( D)  
 I (D j )
j 1 | D |

Information gained by branching on attribute A
Gain(A) Info(D) InfoA(D)
82
Expected Information (Entropy)

Information needed (after using A to split D into v partitions)
v |D |
to classify D:
j
InfoA ( D)  
 I (D j )
j 1 | D |
Info ( D) 
2
3
Info (1,1)  Info (2,1)
5
5
Info ( D) 
2
3
Info (2,0)  Info (3,0)
5
5
83
Attribute Selection: Information Gain


Class P: buys_computer = “yes”
Class N: buys_computer = “no”
Info ( D)  I (9,5)  
age
age
<=30
<=30
31…40
>40
>40
>40
31…40
<=30
<=30
>40
<=30
31…40
31…40
>40
Infoage ( D ) 
9
9
5
5
log 2 ( )  log 2 ( ) 0.940
14
14 14
14
pi
ni
I(pi, ni)
<=30
2
3
0.971
31…40
>40
4
3
0
2
0
0.971
income student credit_rating
high
no
fair
high
no
excellent
high
no
fair
medium
no
fair
low
yes fair
low
yes excellent
low
yes excellent
medium
no
fair
low
yes fair
medium
yes fair
medium
yes excellent
medium
no
excellent
high
yes fair
medium
no
excellent

5
4
I ( 2,3) 
I ( 4,0)
14
14
5
I (3,2)  0.694
14
5
I ( 2,3) means “age <=30” has 5 out of
14
14 samples, with 2 yes’es and 3
no’s. Hence
buys_computer
no
no
yes
yes
yes
no
yes
no
yes
yes
yes
yes
yes
no
Gain(age)  Info(D)  Infoage (D)  0.246
Similarly,
Gain(income)  0.029
Gain( student )  0.151
Gain(credit _ rating )  0.048
84
Gain Ratio for Attribute Selection (C4.5)
• Information gain measure is biased towards attributes with a
large number of values.
• C4.5 (a successor of ID3) uses gain ratio to overcome the
problem (normalization to information gain.)
v
SplitInfoA ( D)  
j 1
| Dj |
| D|
 log2 (
| Dj |
| D|
)
– GainRatio(A) = Gain(A)/SplitInfo(A)
SplitInfo A ( D)  
4
4
6
6
4
4
 log 2 ( )   log 2 ( )   log 2 ( )  0.926
14
14 14
14 14
14
GainRatio(income) = 0.029/0.926 = 0.031
• The attribute with the maximum gain ratio is selected as the
splitting attribute.
85
Gini index (CART, IBM IntelligentMiner)
• If a data set D contains examples from n classes, gini index, Gini(D) is
n
defined as
2
Gini( D) 1
 pj
j 1
where pj is the relative frequency of class j in D
• If a data set D is split on A into two subsets D1 and D2, the gini index Gini(D)
is defined as:
|D1|
|D2 |
(
D
)

Gini
(
)

Gini( D 2)
Gini A
D1
|D|
|D|
• Reduction in Impurity:
Gini( A)  Gini(D)  GiniA (D)
• The attribute provides the smallest GiniA(D) (or the largest reduction in
impurity) is chosen to split the node (need to enumerate all the possible
splitting points for each attribute.)
86
Gini index (CART, IBM IntelligentMiner)
• Ex. D has 9 tuples in buys_computer = “yes” and 5 in “no.”
2
2
9 5
Gini( D)  1        0.459
 14   14 
• Suppose the attribute income partitions D into 10 in D1: {low, medium}
and 4 in D2: {high}.
 10 
4
Giniincome{low,medium} ( D)   Gini( D1 )   Gini( D1 )
 14 
 14 
10
6
4
4
1
3
 [1  ( ) 2  ( ) 2 ]  [1  ( ) 2  ( ) 2 ]
14
10
10
14
4
4
 0.45
 Giniincome{high} ( D)
But Giniincomeϵ{medium,high} is 0.30 and thus the best since it is the lowest.
87
Other Attribute Selection Measures
• CHAID: a popular decision tree algorithm, measure based on χ2 test for
independence
• C-SEP: performs better than info. gain and gini index in certain cases
• G-statistics: has a close approximation to χ2 distribution
• MDL (Minimal Description Length) principle (i.e., the simplest solution is preferred):
– The best tree as the one that requires the fewest # of bits to both (1)
encode the tree, and (2) encode the exceptions to the tree
• Multivariate splits (partition based on multiple variable combinations)
– CART: finds multivariate splits based on a linear combination of attributes.
Which attribute selection measure is the best?
Most give good results, none is significantly superior than others
88
Other Types of Classification Methods
• Bayes Classification Methods
• Rule-Based Classification
• Support Vector Machine (SVM)
• Some of these methods will be taught in the
following lessons.
89
CLUSTERING
90
What is Cluster Analysis?
• Cluster: a collection of data objects
– Similar to one another within the same cluster
– Dissimilar to the objects in other clusters
• Cluster Analysis
– Grouping a set of data objects into clusters
• Typical applications:
– As a stand-alone tool to get insight into data
distribution
– As a preprocessing step for other algorithms
91
General Applications of Clustering
• Spatial data analysis
– Create thematic maps in GIS by clustering feature spaces.
– Detect spatial clusters and explain them in spatial data mining.
•
•
•
•
Image Processing
Pattern recognition
Economic Science (especially market research)
WWW
– Document classification
– Cluster Web-log data to discover groups of similar access
patterns
92
Examples of Clustering Applications
• Marketing: Help marketers discover distinct groups in their
customer bases, and then use this knowledge to develop
targeted marketing programs.
• Land use: Identification of areas of similar land use in an earth
observation database.
• Insurance: Identifying groups of motor insurance policy
holders with a high average claim cost.
• City-planning: Identifying groups of houses according to their
house type, value, and geographical location.
93
What is Good Clustering?
• A good clustering method will produce high quality
clusters with
– High intra-class similarity
– Low inter-class similarity
• The quality of a clustering result depends on both the
similarity measure used by the method and its
implementation.
• The quality of a clustering method is also measured by
its ability to discover hidden patterns.
94
Requirements of Clustering
in Data Mining (1/2)
• Scalability
• Ability to deal with different types of
attributes
• Discovery of clusters with arbitrary shape
• Minimal requirements of domain knowledge
for input
• Able to deal with outliers
95
Requirements of Clustering
in Data Mining (2/2)
• Insensitive to order of input records
• High dimensionality
– Curse of dimensionality
• Incorporation of user-specified constraints
• Interpretability and usability
96
Clustering Methods (I)
• Partitioning Method
– Construct various partitions and then evaluate them by some criterion,
e.g., minimizing the sum of square errors
– K-means, k-medoids, CLARANS
• Hierarchical Method
– Create a hierarchical decomposition of the set of data (or objects)
using some criterion
– Diana, Agnes, BIRCH, ROCK, CHAMELEON
• Density-based Method
– Based on connectivity and density functions
– Typical methods: DBSACN, OPTICS, DenClue
97
Clustering Methods (II)
• Grid-based approach
– based on a multiple-level granularity structure
– Typical methods: STING, WaveCluster, CLIQUE
• Model-based approach
– A model is hypothesized for each of the clusters and tries to find the best fit of
that model to each other
– Typical methods: EM, SOM, COBWEB
• Frequent pattern-based
– Based on the analysis of frequent patterns
– Typical methods: pCluster
• User-guided or constraint-based
– Clustering by considering user-specified or application-specific constraints
– Typical methods: cluster-on-demand, constrained clustering
98
Typical Alternatives to
Calculate the Distance between Clusters
• Single link: smallest distance between an element in one cluster and an
element in the other, i.e., dis(Ki, Kj) = min(tip, tjq)
• Complete link: largest distance between an element in one cluster and
an element in the other, i.e., dis(Ki, Kj) = max(tip, tjq)
• Average: average distance between an element in one cluster and an
element in the other, i.e., dis(Ki, Kj) = avg(tip, tjq)
• Centroid: distance between the centroids of two clusters,
i.e., dis(Ki, Kj) = dis(Ci, Cj)
• Medoid: distance between the medoids of two clusters,
i.e., dis(Ki, Kj) = dis(Mi, Mj)
– Medoid: one chosen, centrally located object in the cluster
99
Centroid, Radius and Diameter of a Cluster
(for numerical data sets)
• Centroid: the “middle” of a cluster
Cm 
iN 1(t
ip
)
N
• Radius: square root of average mean squared distance from any point of
the cluster to its centroid
 N (t  cm ) 2
Rm  i 1 ip
N
• Diameter: square root of average mean squared distance between all
pairs of points in the cluster
 N  N (t  t ) 2
i  1 j  1 ip jq
D 
m
N ( N  1)
diameter != 2 * radius
100
Partitioning Algorithms: Basic Concept
• Partitioning method: construct a partition of a
database D of n objects into a set of k clusters.
• Given a number k, find a partition of k clusters
that optimizes the chosen partitioning criterion.
– Global optimal: exhaustively enumerate all partitions.
– Heuristic methods: k-means, k-medoids
• k-means (MacQueen’67)
• k-medoids or PAM, partion around medoids (Kaufman &
Rousseeuw’87)
101
The K-Means Clustering Method
• Given k, the k-means algorithm is implemented in
four steps:
loop
1. Arbitrarily choose k points as initial cluster centroids.
2. Update Means (Centroids): Compute seed points as
the center of the clusters of the current partition.
(center: mean point of the cluster)
3. Re-assign Points: Assign each object to the cluster with
the nearest seed point.
4. Go back to Step 2, stop when no more new assignment.
102
Example of the
K-Means Clustering Method
10
10
9
9
8
8
7
7
6
6
5
5
10
9
8
7
6
5
4
3
2
1
0
0
1
2
3
4
5
6
7
8
Given k = 2:
Arbitrarily choose k
object as initial
cluster centroid
9
10
Assign
each
objects
to the
most
similar
centroid
4
3
2
1
0
0
1
2
3
4
5
6
7
8
9
10
Update
the
cluster
means
4
3
2
1
0
0
1
2
3
4
5
6
Re-assign
10
9
9
8
8
7
7
6
6
5
4
3
2
1
0
1
2
3
4
5
6
7
8
9
8
9
10
Re-assign
10
0
7
10
Update
the
cluster
means
5
4
3
2
1
0
0
1
2
3
4
5
6
7
8
9
103
10
Comments on the K-Means Clustering
• Time Complexity: O(tkn), where n is # of objects, k is
# of clusters, and t is # of iterations. Normally, k,t<<n.
• Often terminates at a local optimum.
(The global optimum may be found using techniques such as:
deterministic annealing and genetic algorithms)
• Weakness:
– Applicable only when mean is defined, how about
categorical data?
– Need to specify k, the number of clusters, in advance
– Unable to handle noisy data and outliers
104
Why is K-Means Unable to
Handle Outliers?
• The k-means algorithm is sensitive to outliers
– Since an object with an extremely large value may
substantially distort the distribution of the data.
X
• K-Medoids: Instead of taking the mean value of the
object in a cluster as a reference point, medoids can
be used, which is the most centrally located object in
a cluster.
105
PAM: The K-Medoids Method
• PAM: Partition Around Medoids
• Use real object to represent the cluster
1. Randomly select k representative objects as medoids.
2. Assign each data point to the closest medoid.
3. For each medoid m,
loop
a.
For each non-medoid data point o
b.
Swap m and o, and compute the total cost of the configuration.
4. Select the configuration with the lowest cost.
5. Repeat steps 2 to 5 until there is no change in the medoid.
106
A Typical K-Medoids Algorithm (PAM)
10
10
9
9
8
8
7
7
Arbitrary
choose k
object as
initial
medoids
6
5
4
3
2
1
0
0
1
2
3
4
5
6
7
8
9
Assign each
remaining object to
the nearest medoid
6
5
4
3
2
1
0
10
0
10
1
2
3
4
5
6
7
8
9
10
9
8
k=2
7
10
6
m2
9
8
5
4
7
3
6
2
m1
5
4
1
0
0
3
1
2
3
4
5
6
7
8
9
2
1
0
0
1
2
3
4
5
6
7
8
9
10
Swap each medoid and each data point, and
compute the total cost of the configuration
107
10
PAM Clustering: Total swapping cost TCih=jCjih
10
10
d(j,h)<d(j,t)
9
- Original
medoid: t, i
- h: swap with i
t
8
7
j
6
5
6
5
i
4
3
- j: any nonselected object
t
8
7
j
9
h
h
i
4
3
j
2
1
0
0
1
2
3
4
5
6
7
8
9
10
i
j
h
Cjih = d(j, h) - d(j, i)
j
2
1
t
0
0
1
2
3
4
5
6
7
8
9
10
j
t
Cjih = 0
10
10
d(j,h)>d(j,t)
9
h
8
9
8
j
7
6
7
6
i
5
5
i
4
t
j
2
1
0
1
2
3
4
5
6
j
3
3
0
h
4
7
8
9
10
Cjih = d(j, t) - d(j, i)
i
j
t
j
t
2
1
0
0
1
2
3
4
5
6
7
8
9
10
Cjih = d(j, h) - d(j, t)
t
j
h
108
What is the Problem with PAM?
• PAM is more robust than k-means in the presence of
noise and outliers because a medoid is less influenced
by outliers or other extreme values than a mean.
• PAM works efficiently for small data sets but does not
scale well for large data sets.
– O( k(n-k)(n-k) ) for each iteration,
where n is # of data, k is # of clusters
– Improvements: CLARA (uses a sampled set to determine
medoids), CLARANS
109
Hierarchical Clustering
• Use distance matrix as clustering criteria.
• This method does not require the number of clusters k as an
input, but needs a termination condition.
Step 0
a
Step 1
Step 2 Step 3 Step 4
agglomerative
(AGNES)
ab
b
abcde
c
cde
d
de
e
Step 4
Step 3
Step 2 Step 1 Step 0
divisive
(DIANA)
110
AGNES (Agglomerative Nesting)
•
•
•
•
•
Introduced in Kaufmann and Rousseeuw (1990)
Use the Single-Link method and the dissimilarity matrix.
Merge nodes that have the least dissimilarity
Go on in a non-descending fashion
Eventually all nodes belong to the same cluster
10
10
10
9
9
9
8
8
8
7
7
7
6
6
6
5
5
5
4
4
4
3
3
3
2
2
2
1
1
1
0
0
0
1
2
3
4
5
6
7
8
9
10
0
0
1
2
3
4
5
6
7
8
9
10
0
1
2
3
4
5
6
7
8
9
10
111
Dendrogram:
Shows How the Clusters are Merged


Decompose data objects into a several levels of nested
partitioning (tree of clusters), called a dendrogram.
A clustering of the data objects is obtained by cutting the
dendrogram at the desired level, then each connected
component forms a cluster.
112
DIANA (Divisive Analysis)
• Introduced in Kaufmann and Rousseeuw (1990)
• Inverse order of AGNES
• Eventually each node forms a cluster on its own.
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More on Hierarchical Clustering
• Major weakness:
– Do not scale well: time complexity is at least O(n2), where n is
the number of total objects.
– Can never undo what was done previously.
• Integration of hierarchical with distance-based clustering
– BIRCH(1996): uses CF-tree data structure and incrementally
adjusts the quality of sub-clusters.
– CURE(1998): selects well-scattered points from the cluster and
then shrinks them towards the center of the cluster by a
specified fraction.
114
Density-Based Clustering Methods
• Clustering based on density (local cluster criterion), such as
density-connected points
• Major features:
– Discover clusters of arbitrary shape
– Handle noise
– One scan
– Need density parameters as termination condition
• Several interesting studies:
– DBSCAN: Ester, et al. (KDD’96)
– OPTICS: Ankerst, et al (SIGMOD’99).
– DENCLUE: Hinneburg & D. Keim (KDD’98)
– CLIQUE: Agrawal, et al. (SIGMOD’98) (more grid-based)
115
Density-Based Clustering: Basic Concepts
• Two parameters:
– Eps: Maximum radius of the neighborhood
– MinPts: Minimum number of points in an Eps-neighborhood
of that point
Eps
116
Density-Based Clustering: Basic Concepts
• Two parameters:
– Eps: Maximum radius of the neighborhood
– MinPts: Minimum number of points in an Eps-neighborhood
of that point
• NEps(q): {p | dist(p,q) <= Eps} // p, q are two data points
• Directly density-reachable: A point p is directly densityreachable from a point q w.r.t. Eps, MinPts if
– p belongs to NEps(q)
– core point condition:
p
q
MinPts = 5
Eps = 1 cm
|NEps (q)| >= MinPts
117
Density-Reachable and Density-Connected
• Density-reachable:
– A point p is density-reachable from a
point q w.r.t. Eps, MinPts if there is a
chain of points p1, …, pn,
p1 = q, pn = p such that pi+1 is directly
density-reachable from pi.
p
p2
q
• Density-connected:
– A point p is density-connected to a
point q w.r.t. Eps, MinPts if there is a
point o such that both, p and q are
density-reachable from o w.r.t. Eps
and MinPts.
p
q
o
118
DBSCAN: Density Based Spatial Clustering of
Applications with Noise
• Relies on a density-based notion of cluster: A cluster is defined
as a maximal set of density-connected points.
• Discovers clusters of arbitrary shape in spatial databases with
noise.
Border
Border
Eps = 1cm
Core
MinPts = 5
119
DBSCAN: The Algorithm
• Arbitrary select an unvisited point p.
• Retrieve all points density-reachable from p w.r.t. Eps and
MinPts.
• If p is a core point, a cluster is formed. Mark all these points
as visited.
• If p is a border point (no points are density-reachable from p),
mark p as visited and DBSCAN visits the next point of the
database.
• Continue the process until all of the points have been visited.
120
References (1)
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R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional
data for data mining applications. SIGMOD'98
M. R. Anderberg. Cluster Analysis for Applications. Academic Press, 1973.
M. Ankerst, M. Breunig, H.-P. Kriegel, and J. Sander. Optics: Ordering points to identify the clustering
structure, SIGMOD’99.
P. Arabie, L. J. Hubert, and G. De Soete. Clustering and Classification. World Scientific, 1996
Beil F., Ester M., Xu X.: "Frequent Term-Based Text Clustering", KDD'02
M. M. Breunig, H.-P. Kriegel, R. Ng, J. Sander. LOF: Identifying Density-Based Local Outliers. SIGMOD 2000.
M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large
spatial databases. KDD'96.
M. Ester, H.-P. Kriegel, and X. Xu. Knowledge discovery in large spatial databases: Focusing techniques for
efficient class identification. SSD'95.
D. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2:139-172, 1987.
D. Gibson, J. Kleinberg, and P. Raghavan. Clustering categorical data: An approach based on dynamic
systems. VLDB’98.
121
References (2)
•
•
•
•
•
•
•
•
•
•
•
•
•
•
V. Ganti, J. Gehrke, R. Ramakrishan. CACTUS Clustering Categorical Data Using Summaries. KDD'99.
D. Gibson, J. Kleinberg, and P. Raghavan. Clustering categorical data: An approach based on dynamic systems. In
Proc. VLDB’98.
S. Guha, R. Rastogi, and K. Shim. Cure: An efficient clustering algorithm for large databases. SIGMOD'98.
S. Guha, R. Rastogi, and K. Shim. ROCK: A robust clustering algorithm for categorical attributes. In ICDE'99, pp. 512521, Sydney, Australia, March 1999.
A. Hinneburg, D.l A. Keim: An Efficient Approach to Clustering in Large Multimedia Databases with Noise. KDD’98.
A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Printice Hall, 1988.
G. Karypis, E.-H. Han, and V. Kumar. CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling.
COMPUTER, 32(8): 68-75, 1999.
L. Kaufman and P. J. Rousseeuw, 1987. Clustering by Means of Medoids. In: Dodge, Y. (Ed.), Statistical Data Analysis
Based on the L1 Norm, North Holland, Amsterdam. pp. 405-416.
L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons,
1990.
E. Knorr and R. Ng. Algorithms for mining distance-based outliers in large datasets. VLDB’98.
J. B. MacQueen (1967): "Some Methods for classification and Analysis of Multivariate Observations", Proceedings
of 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press,
1:281-297
G. J. McLachlan and K.E. Bkasford. Mixture Models: Inference and Applications to Clustering. John Wiley and Sons,
1988.
P. Michaud. Clustering techniques. Future Generation Computer systems, 13, 1997.
R. Ng and J. Han. Efficient and effective clustering method for spatial data mining. VLDB'94.
122
References (3)
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•
•
•
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L. Parsons, E. Haque and H. Liu, Subspace Clustering for High Dimensional Data: A Review , SIGKDD
Explorations, 6(1), June 2004
E. Schikuta. Grid clustering: An efficient hierarchical clustering method for very large data sets. Proc. 1996
Int. Conf. on Pattern Recognition,.
G. Sheikholeslami, S. Chatterjee, and A. Zhang. WaveCluster: A multi-resolution clustering approach for
very large spatial databases. VLDB’98.
A. K. H. Tung, J. Han, L. V. S. Lakshmanan, and R. T. Ng. Constraint-Based Clustering in Large Databases,
ICDT'01.
A. K. H. Tung, J. Hou, and J. Han. Spatial Clustering in the Presence of Obstacles , ICDE'01
H. Wang, W. Wang, J. Yang, and P.S. Yu. Clustering by pattern similarity in large data sets, SIGMOD’ 02.
W. Wang, Yang, R. Muntz, STING: A Statistical Information grid Approach to Spatial Data Mining, VLDB’97.
T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH : an efficient data clustering method for very large
databases. SIGMOD'96.
Wikipedia: DBSCAN. http://en.wikipedia.org/wiki/DBSCAN.
123
MORE ABOUT DATA MINING
124
http://www.cs.uvm.edu/~xwu/PPT/ICDM10-Sydney/ICDM10-Keynote.pdf
ICDM ’10 KEYNOTE SPEECH
“10 YEARS OF DATA MINING RESEARCH:
RETROSPECT AND PROSPECT”
Xindong Wu, University of Vermont, USA
125
The Top 10 Algorithms
The 3-Step Identification Process
1. Nominations. ACM KDD Innovation Award and IEEE
ICDM Research Contributions Award winners were
invited in September 2006 to each nominate up to 10
best-known algorithms.
2. Verification. Each nomination was verified for its
citations on Google Scholar in late October 2006, and
those nominations that did not have at least 50
citations were removed. 18 nominations survived and
were then organized in 10 topics.
3. Voting by the wider community.
126
Top-10 Most Popular DM Algorithms:
18 Identified Candidates (I)
•
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Classification
– #1. C4.5: Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann.,
1993.
– #2. CART: L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and
Regression Trees. Wadsworth, 1984.
– #3. K Nearest Neighbors (kNN): Hastie, T. and Tibshirani, R. 1996. Discriminant
Adaptive Nearest Neighbor Classification. TPAMI. 18(6)
– #4. Naive Bayes Hand, D.J., Yu, K., 2001. Idiot's Bayes: Not So Stupid After All?
Internat. Statist. Rev. 69, 385-398.
Statistical Learning
– #5. SVM: Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. SpringerVerlag.
– #6. EM: McLachlan, G. and Peel, D. (2000). Finite Mixture Models. J. Wiley, New
York. Association Analysis
Association Analysis
– #7. Apriori: Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining
Association Rules. In VLDB '94.
– #8. FP-Tree: Han, J., Pei, J., and Yin, Y. 2000. Mining frequent patterns without
candidate generation. In SIGMOD '00.
127
The 18 Identified Candidates (II)
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Link Mining
– #9. PageRank: Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual
Web search engine. In WWW-7, 1998.
– #10. HITS: Kleinberg, J. M. 1998. Authoritative sources in a hyperlinked
environment. SODA, 1998.
Clustering
– #11. K-Means: MacQueen, J. B., Some methods for classification and analysis of
multivariate observations, in Proc. 5th Berkeley Symp. Mathematical Statistics and
Probability, 1967.
– #12. BIRCH: Zhang, T., Ramakrishnan, R., and Livny, M. 1996. BIRCH: an efficient
data clustering method for very large databases. In SIGMOD '96.
Bagging and Boosting
– #13. AdaBoost: Freund, Y. and Schapire, R. E. 1997. A decision-theoretic
generalization of on-line learning and an application to boosting. J. Comput. Syst.
Sci. 55, 1 (Aug. 1997), 119-139.
128
The 18 Identified Candidates (III)
•
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•
Sequential Patterns
– #14. GSP: Srikant, R. and Agrawal, R. 1996. Mining Sequential Patterns:
Generalizations and Performance Improvements. In Proceedings of the 5th
International Conference on Extending Database Technology, 1996.
– #15. PrefixSpan: J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal and M-C.
Hsu. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern
Growth. In ICDE '01.
Integrated Mining
– #16. CBA: Liu, B., Hsu, W. and Ma, Y. M. Integrating classification and association
rule mining. KDD-98.
Rough Sets
– #17. Finding reduct: Zdzislaw Pawlak, Rough Sets: Theoretical Aspects of Reasoning
about Data, Kluwer Academic Publishers, Norwell, MA, 1992
Graph Mining
– #18. gSpan: Yan, X. and Han, J. 2002. gSpan: Graph-Based Substructure Pattern
Mining. In ICDM '02.
129
Top-10 Algorithm Finally Selected at ICDM’06
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#1: C4.5 (61 votes)
#2: K-Means (60 votes)
#3: SVM (58 votes)
#4: Apriori (52 votes)
#5: EM (48 votes)
#6: PageRank (46 votes)
#7: AdaBoost (45 votes)
#7: kNN (45 votes)
#7: Naive Bayes (45 votes)
#10: CART (34 votes)
130
10 Challenging Problems in Data
Mining Research
•
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•
•
•
•
•
•
•
•
Developing a Unifying Theory of Data Mining
Scaling Up for High Dimensional Data/High Speed Streams
Mining Sequence Data and Time Series Data
Mining Complex Knowledge from Complex Data
Data Mining in a Graph Structured Data
Distributed Data Mining and Mining Multi-agent Data
Data Mining for Biological and Environmental Problems
Data-Mining-Process Related Problems
Security, Privacy and Data Integrity
Dealing with Non-static, Unbalanced and Cost-sensitive Data
131
Advanced Topics in Data Mining
•
•
•
•
•
•
•
•
•
Web & Text Mining
Spatio-temporal Data Mining
Data Stream Mining
Uncertain Data Mining
Privacy Preserving in Data Mining
Graph Mining
Social Network Mining
Visualization of Data Mining
…
132
DATA STREAM MINING
133
Stream
Synopses
Multiple Data Streams
Online Stream
Summarization
Examples:
Sensor network data, network flow
data, stock market data, etc.
Query
Processing
(Approximate)
Results
Stream
Mining
Stream Process System
Characteristics of Data Streams
Data Stream Management
• Arrive in a high speed
• Arrive continuously, and possibly
endlessly
• Have a huge volume
• Design synopsis structures for
streams
• Design real-time and approximate
algorithms for stream mining and
query processing
134
GRAPH MINING
135
Why Graph Mining?
• Graphs are ubiquitous
– Chemical compounds (Cheminformatics)
– Protein structures, biological pathways/networks (Bioinformatics)
– Program control flow, traffic flow, and workflow analysis
– XML databases, Web, and social network analysis
• Graph is a general model
– Trees, lattices, sequences, and items are degenerated graphs
• Diversity of graphs
– Directed vs. undirected, labeled vs. unlabeled (edges & vertices), weighted,
with angles & geometry (topological vs. 2-D/3-D)
• Complexity of algorithms: many problems are of high complexity
136
Graph Pattern Mining
• Frequent sub-graphs
– A (sub-)graph is frequent if its support (occurrence
frequency) in a given dataset is no less than a
minimum support threshold.
• Applications of graph pattern mining
–
–
–
–
Mining biochemical structures
Program control flow analysis
Mining XML structures or Web communities
Building blocks for graph classification, clustering,
compression, comparison, and correlation analysis
137
Example: Frequent Subgraphs
• Graph dataset
(A)
(B)
(C)
FREQUENT PATTERNS
(MIN SUPPORT IS 2)
(1)
(2)
138
Graph Mining Algorithms
• Incomplete beam search – Greedy (Subdue: Holder et al. KDD’94)
• Inductive logic programming (WARMR: Dehaspe et al. KDD’98)
• Graph theory-based approaches
– Apriori-based approach
• AGM/AcGM: Inokuchi, et al. (PKDD’00), FSG: Kuramochi and Karypis
(ICDM’01), PATH#: Vanetik and Gudes (ICDM’02, ICDM’04), FFSM: Huan,
et al. (ICDM’03)
– Pattern-growth approach
• MoFa: Borgelt and Berthold (ICDM’02), gSpan: Yan and Han
(ICDM’02), Gaston: Nijssen and Kok (KDD’04)
139
SOCIAL NETWORK MINING
140
What is Social Network?
Nodes: individuals
Links: social relationship
(family/work/friendship/etc.)
Social Network:
Many individuals with diverse
social interactions between them.
141
Example of Social Networks
Friendship network
node: person
link: acquaintanceship
http://nexus.ludios.net/view/demo/
142
Example of Social Networks
Co-author network
node: author
link: write papers together
Ke, Visvanath & Börner, 2004
143
Mining on Social Networks
• Social network analysis has a long history in social sciences.
– A summary of the progress has been written. Linton Freeman, “The
Development of Social Network Analysis.” Vancouver: Empirical Pres, 2006.
• Today: Convergence of social and technological networks, computing and
info. systems with intrinsic social structure. (By Jon Kleinberg, Cornell
University)
• Relevant Topics:
–
–
–
–
–
How to build a suitable model for search and diffusion in social networks.
Link mining in a multi-relational, heterogeneous and semi-structured network.
Community formation, clustering of social network data.
Abstract or summarization of social networks.
Privacy preserving in social network data.
and many others…
144
THANK YOU!
145