Chapter 4: Data Mining Primitives, Languages,
and System Architectures

Data mining primitives: What defines a data
mining task?

A data mining query language

Design graphical user interfaces based on a data
mining query language

Architecture of data mining systems

Summary
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Unit II

Data Mining Primitive, Languages, and
System Architecture :
 Data mining primitive,
 Data Mining Query Languages,
 Designing Graphical User Interfaces Based on
a Data Mining Query Language
 Architecture of Data Mining Systems
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


Misconception: Data mining systems can autonomously dig out all of the valuable
knowledge from a given large database, without human intervention.
If there was no user intervention then the system would uncover a large set of
patterns that may even surpass the size of the database. Hence, user interference is
required.
This user communication with the system is provided by using a set of data mining
primitives.
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Why Data Mining Primitives and Languages?





A popular misconception about data mining is to expect that
data mining systems can autonomously dig out all of the
valuable knowledge and patterns that is embedded in large
database, without human intervention or guidance.
Finding all the patterns autonomously in a database? —
unrealistic because the patterns could be too many but
uninteresting
Data mining should be an interactive process
 User directs what to be mined
Users must be provided with a set of primitives to be used to
communicate with the data mining system
Incorporating these primitives in a data mining query language
 More flexible user interaction
 Foundation for design of graphical user interface
 Standardization of data mining industry and practice
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Data Mining Primitives :
What Defines a Data Mining Task ?





Task-relevant data : What is the data set I want to mine?
Type of knowledge to be mined : What kind of
knowledge do I want to mine ?
Background knowledge : What background knowledge
could be useful here ?
Pattern interestingness measurements : What measures
can be useful to estimate pattern interestingness ?
Visualization of discovered patterns : How do I want the
discovered patterns to be presented ?
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Primitives for specifying a data mining task
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Task-Relevant Data (Minable View)





The first primitive is the specification of the data on
which mining is to be performed.
Typically, a user is interested in only a subset of the
database. It is impractical to mine the entire database,
particularly since the number of patterns generated could
be exponential w.r.t the database size.
Furthermore, many of the patterns found would be
irrelevant to the interests of the user.
In a relational database, the set of task relevant data can
be collected via a relational query involving operations
like selection, projection, join and aggregation.
This retrieval of data can be thought of as a “subtask” of
the data mining task. The data collection process results
in a new data relational called the initial data relation
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




The initial data relation can be ordered or grouped
according to the conditions specified in the query.
The data may be cleaned or transformed (e.g.
aggregated on certain attributes) prior to applying data
mining analysis.
This initial relation may or may not correspond to
physical relation in the database.
Since virtual relations are called Views in the field of
databases, the set of task-relevant data for data mining
is called a minable view
If data mining task is to study associations between
items frequently purchased at AllElectronics by customers
in Canada, the task relevant data can be specified by
providing the following information
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Task-Relevant Data (Minable View)

Database or data warehouse name

Database tables or data warehouse cubes

Condition for data selection

Relevant attributes or dimensions

Data grouping criteria
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Task relevant data


Data portion to be investigated.
Attributes of interest (relevant attributes) can be
specified.

Initial data relation

Minable view
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Example
If a data mining task is to study associations between items
frequently purchased at All Electronics by customers in Canada, the
task relevant data can be specified by providing the following
information:

Name of the database or data warehouse to be used (e.g.,
AllElectronics_db)

Names of the tables or data cubes containing relevant data
(e.g., item, customer, purchases and items_sold)

Conditions for selecting the relevant data (e.g., retrieve data
pertaining to purchases made in Canada for the current
year)

The relevant attributes or dimensions (e.g., name and price
from the item table and income and age from the customer
table)
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The kind of knowledge to be mined
It is important to specify the kind of knowledge to be
mined, as this determines the data mining functions to be
performed.
The kinds of knowledge include concept description
(characterization
and
discrimination),
association,
classification, predication, clustering, and evolution analysis.
In addition to specifying the kind of knowledge to be mined
for a given data mining task, the user can be more specific
and provide pattern templates that all discovered patterns
must match
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The kind of knowledge to be mined
These templates, or metapatterns (also called metarules or
metaqueries), can be used to guide the discovery process. The
use of metapatterns is illustrated in the following example.
A user studying the buying habits of Allelectronics customers may
choose to mine association rules of the form:
P (X:customer,W) ^ Q (X,Y) => buys (X,Z)
Here X is a key of the customer relations, P & Q are predicate
variables and W,Y and Z are object variables
[1.4%, 70%]
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The kind of knowledge to be mined
The search for association rules is confined to those matching the given
metarule, such as
age (X, “30…..39”) ^ income (X, “40k….49K”) => buys (X,
“VCR”)
[2.2%, 60%]
and
occupation (X, “student ”) ^ age (X, “20…..29”)=> buys (X,
“computer”)
[1.4%, 70%]
The former rule states that customers in their thirties, with an annual
income of between 40K and 49K, are likely (with 60% confidence) to
purchase a VCR, and such cases represent about 2.2.% of the total
number of transactions.
The latter rule states that customers who are students and in their
twenties are likely (with 70% confidence) to purchase a computer, and
such cases represent about 1.4% of the total number of transactions.
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Types of knowledge to be mined

Characterization

Discrimination

Association

Classification/prediction

Clustering

Outlier analysis

Other data mining tasks
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The data mining functionalities and the variety of knowledge
they discover are briefly presented in the following list:
Characterization: Data characterization is a summarization
of general features of objects in a target class, and produces
what is called characteristic rules. The data relevant to a userspecified class are normally retrieved by a database query and
run through a summarization module to extract the essence of
the data at different levels of abstractions. For example, one
may want to characterize the OurVideoStore customers who
regularly rent more than 30 movies a year. With concept
hierarchies on the attributes describing the target class, the
attribute-oriented induction method can be used, for example,
to carry out data summarization. Note that with a data cube
containing summarization of data, simple OLAP operations fit
the purpose of data characterization.
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Discrimination: Data discrimination produces what are
called discriminant rules and is basically the comparison of the
general features of objects between two classes referred to as
the target class and the contrasting class. For example, one
may want to compare the general characteristics of the
customers who rented more than 30 movies in the last year
with those whose rental account is lower than 5. The
techniques used for data discrimination are very similar to the
techniques used for data characterization with the exception
that data discrimination results include comparative measures.
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Association analysis: Association analysis is the discovery
of what are commonly called association rules. It studies the
frequency of items occurring together in transactional
databases, and based on a threshold called support, identifies
the frequent item sets. Another threshold, confidence, which
is the conditional probability than an item appears in a
transaction when another item appears, is used to pinpoint
association rules. Association analysis is commonly used for
market basket analysis. For example, it could be useful for the
OurVideoStore manager to know what movies are often
rented together or if there is a relationship between renting a
certain type of movies and buying popcorn or pop. The
discovered association rules are of the form: P -> Q [s,c],
where P and Q are conjunctions of attribute value-pairs, and s
(for support) is the probability that P and Q appear together
in a transaction and c (for confidence) is the conditional
probability that Q appears in a transaction when P is present.
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For
example,
the
hypothetic
association
rule:
RentType(X, "game") AND Age(X, "13-19") -> Buys(X, "pop")
[s=2%
,c=55%]
would indicate that 2% of the transactions considered are of
customers aged between 13 and 19 who are renting a game
and buying a pop, and that there is a certainty of 55% that
teenage customers who rent a game also buy pop.
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Classification: Classification analysis is the organization of
data in given classes. Also known as supervised classification,
the classification uses given class labels to order the objects in
the data collection. Classification approaches normally use a
training set where all objects are already associated with
known class labels. The classification algorithm learns from
the training set and builds a model. The model is used to
classify new objects. For example, after starting a credit
policy, the OurVideoStore managers could analyze the
customers’ behaviours vis-à-vis their credit, and label
accordingly the customers who received credits with three
possible labels "safe", "risky" and "very risky". The
classification analysis would generate a model that could be
used to either accept or reject credit requests in the future.
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Prediction: Prediction has attracted considerable attention
given the potential implications of successful forecasting in a
business context. There are two major types of predictions:
one can either try to predict some unavailable data values or
pending trends, or predict a class label for some data. The
latter is tied to classification. Once a classification model is
built based on a training set, the class label of an object can
be foreseen based on the attribute values of the object and
the attribute values of the classes. Prediction is however more
often referred to the forecast of missing numerical values, or
increase/ decrease trends in time related data. The major
idea is to use a large number of past values to consider
probable future values.
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Clustering: Similar to classification, clustering is the
organization of data in classes. However, unlike classification,
in clustering, class labels are unknown and it is up to the
clustering algorithm to discover acceptable classes. Clustering
is also called unsupervised classification, because the
classification is not dictated by given class labels. There are
many clustering approaches all based on the principle of
maximizing the similarity between objects in a same class
(intra-class similarity) and minimizing the similarity between
objects of different classes (inter-class similarity).
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Outlier analysis: Outliers are data elements that cannot be
grouped in a given class or cluster. Also known as exceptions
or surprises, they are often very important to identify. While
outliers can be considered noise and discarded in some
applications, they can reveal important knowledge in other
domains, and thus can be very significant and their analysis
valuable.
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Evolution and deviation analysis: Evolution and deviation
analysis pertain to the study of time related data that changes
in time. Evolution analysis models evolutionary trends in data,
which consent to characterizing, comparing, classifying or
clustering of time related data. Deviation analysis, on the
other hand, considers differences between measured values
and expected values, and attempts to find the cause of the
deviations from the anticipated values.
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Background Knowledge: Concept Hierarchies




It is the information about the domain to be mined
Concept hierarchy: is a powerful form of background
knowledge. It allows the discovery of knowledge at
multiple level of abstraction.
Concept hierarchy defines a sequence of mappings from a
set of low – level concepts to higher – level, more general
concepts. A concept hierarchy for the dimension location
is shown in figure, mapping low-level concepts (i.e. cities)
to more general concepts (i.e. countries)
Concept hierarchy consists of four levels. In our example,
level 1 represents the concept country, while levels 2 and
3 represents the concepts province_or_state and city resp
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Example
Level 0
all
British
Columbia
Vancouver
March 22, 2016
Ontario
Victoria
Level 1
USA
Canada
Toronto
New York
Ottawa
New York
Data Mining: Concepts and Techniques
Buffalo
Illinois
Level 2
Chicago
Level 3
26

Four major types of concept hierarchies:
Schema hierarchies
Set-grouping hierarchies
Operation-derived hierarchies
Rule-based hierarchies
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Background Knowledge: Concept Hierarchies




Schema hierarchy
 E.g., street < city < province_or_state < country
Set-grouping hierarchy
 E.g., {20-39} = young, {40-59} = middle_aged
Operation-derived hierarchy
 email address: dmbook@cs.sfu.ca
login-name < department < university < country
Rule-based hierarchy
 low_profit_margin (X) <= price(X, P1) and cost (X,
P2) and (P1 - P2) < $50
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Concept hierarchies (2)




Rolling Up - Generalization of data
Allows to view data at more meaningful and explicit
abstractions.
Makes it easier to understand
Compresses the data
Would require fewer input/output operations
Drilling Down - Specialization of data
Concept values replaced by lower level concepts
There may be more than concept hierarchy for a given
attribute or dimension based on different user viewpoints
Example:
Regional sales manager may prefer the previous concept
hierarchy but marketing manager might prefer to see
location with respect to linguistic lines in order to facilitate
the distribution of commercial ads.
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Schema hierarchies




Schema hierarchy is the total or partial order among
attributes in the database schema.
Schema hierarchy may formally express existing semantic
relationships between attributes.
Typically a schema hierarchy specifies a data warehouse
dimension
Example: location hierarchy
street < city < province/state < country


This means that street is at conceptually lower level than
city, which is lower than province_or_state, which is
conceptually lower than country.
A schema hierarchy provides metadata information.
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DWM 3.1
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30
Set-grouping hierarchies

Organizes values for a given attribute into groups or sets
or range of values.
Total or partial order can be defined among groups.
Used to refine or enrich schema-defined hierarchies.
Typically used for small sets of object relationships.

Example: Set-grouping hierarchy for age



{young, middle_aged, senior}
{20….29} young
{40….59} middle_aged
{60….89} senior
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all (age)
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Operation-derived hierarchies

Operation-derived: An operation derived hierarchy is
based on operations specified by users, experts, or the
data mining system. Operations may include decoding of
information-encoded strings, information extraction from
complex data objects, and data clustering
Example: URL or email address
xyz@cs.iitm.in gives login name < dept. < univ. < country
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Rule-based hierarchies


Rule-based:
Occurs when either whole or portion of a concept
hierarchy is defined as a set of rules and is evaluated
dynamically based on current database data and rule
definition
Example: Following rules are used to categorize items as low_profit,
medium_profit and high_profit_margin.
low_profit_margin(X) <= price(X,P1)^cost(X,P2)^((P1-P2)<50)
medium_profit_margin(X) <= price(X,P1)^cost(X,P2)^((P1P2)≥50)^((P1-P2)≤250)
high_profit_margin(X) <= price(X,P1)^cost(X,P2)^((P1-P2)>250)
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DWM 3.1
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33
Interestingness measure (1)




Although specification of the task relevant data and of the kind
of knowledge to be mined (e.g. characterization, association,
etc.) may substantially reduce the number of pattern
generated, a data mining process may still generate a large
number of patterns
Typically, only a small fraction of these patterns will actually be
of interest to the given user. Thus, users need to further
confine the number of uninteresting patterns returned by the
process. This can be achieved by specifying interestingness
measures that estimate the simplicity, certainty, utility, and
novelty of patterns
We will see some objective measures of pattern
interestingness. In general, each measure is associated with a
threshold that can be controlled by the user.
Rules that do not meet the threshold are considered
uninteresting, and hence are not presented to the user as
knowledge
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Interestingness measure (1)
Simplicity : A factor contributing to the interestingness of a
pattern is the pattern’s overall simplicity for human
comprehension.
Objective measures of pattern simplicity can be viewed as
functions of the pattern structure, defined in terms of the
pattern size in bits, or the number of attributes or operators
appearing in the pattern.
For example, the more complex the structure of a rule is, the
more difficult it is to interpret, and hence, the less interesting
it is likely to be
Rule Length : It is a simplicity measure
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Interestingness measure (1)
Certainty (Confidence) : Each discovered pattern should
have a measure of certainty associated with it that assesses
the validity or “trustworthiness” of the pattern.
A certainty measure for association rules of the form “A =>B”
where A and B are sets of items, is confidence. Confidence is a
certainty measure. Given a set of task-relevant data tuples the
confidence of “A => B” is defined as
confidence (A=>B) = # tuples containing both A and B
# tuples containing A
A confidence of 85% for the rule buys(X, “computer”) =>
buys (X,“software”) means that 85% of all customers who
purchased a computer also bought software
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Interestingness measure (1)
Utility (Support) : The potential usefulness of a pattern is a
factor defining its interestingness. It can be estimated by a
utility function, such as support. The support of an association
pattern refers to the percentage of task relevant data tuples
(or transactions) for which the pattern is true.


Utility (support) : usefulness of a pattern
support (A=>B) = # tuples containing both A and B
total # of tuples
A support of 30% for the above rule means that 30% of all
customers in the computer department purchased both a computer
and software.
Association rules that satisfy both the minimum confidence
and support threshold are referred to as strong association
rules.
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Interestingness measure (1)
Novelty : Novel patterns are those that contribute new
information or increased performance to the given pattern set.
For ex. A data exception. Another strategy for detecting
novelty is to remove redundant patterns.
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Presentation and visualization


For data mining to be effective, data mining systems
should be able to display the discovered patterns in
multiple forms, such as rules, tables, cross tabs (crosstabulations), pie or bar charts, decision trees, cubes, or
other visual representations.
User must be able to specify the forms of presentation to
be used for displaying the discovered patterns.
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DMQL



Adopts SQL-like syntax
Hence, can be easily integrated with relational
query languages
Defined in BNF grammar
 [ ] represents 0 or one occurrence
 { } represents 0 or more occurrences
 Words in sans serif represent keywords
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
Motivation


A DMQL can provide the ability to support ad-hoc and
interactive data mining
By providing a standardized language like SQL




Hope to achieve a similar effect like that SQL has on
relational database
Foundation for system development and evolution
Facilitate information exchange, technology transfer,
commercialization and wide acceptance
Design

DMQL is designed with the primitives described earlier
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Syntax for DMQL


Syntax for specification of

task-relevant data

the kind of knowledge to be mined

concept hierarchy specification

interestingness measure

pattern presentation and visualization
Putting it all together — a DMQL query
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DMQL-Syntax for task-relevant data
specification







Names of the relevant database or data warehouse,
conditions and relevant attributes or dimensions must be
specified
use database ‹database_name› or use data
warehouse ‹data_warehouse_name›
from ‹relation(s)/cube(s)› [where condition]
in relevance to ‹attribute_or_dimension_list›
order by ‹order_list›
group by ‹grouping_list›
having ‹condition›
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Example
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Syntax for Kind of Knowledge to be Mined




Characterization :
‹Mine_Knowledge_Specification› ::=
mine characteristics [as ‹pattern_name›]
analyze ‹measure(s)›
Example:
mine characteristics as customerPurchasing analyze count%
Discrimination:
‹Mine_Knowledge_Specification› ::=
mine comparison [as ‹ pattern_name›]
for ‹target_class› where ‹target_condition›
{versus ‹contrast_class_i where ‹contrast_condition_i›}
analyze ‹measure(s)›
Example:
Mine comparison as purchaseGroups
for bigspenders where avg(I.price) >= $100
versus budgetspenders where avg(I.price) < $100
analyze count
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Syntax for Kind of Knowledge to be Mined (2)




Association:
‹Mine_Knowledge_Specification› ::=
mine associations [as ‹pattern_name›]
[matching ‹metapattern›]
Example: mine associations as buyingHabits
matching P(X: customer, W) ^ Q(X,Y) => buys (X,Z)
Classification:
‹Mine_Knowledge_Specification› ::=
mine classification [as ‹pattern_name›]
analyze ‹classifying_attribute_or_dimension›
Example: mine classification as classifyCustomerCreditRating
analyze credit_rating
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Syntax for concept hierarchy specification



More than one concept per attribute can be specified
Use hierarchy ‹hierarchy_name› for ‹attribute_or_dimension›
Examples:
Schema concept hierarchy (ordering is important)
 define hierarchy location_hierarchy on address as
[street,city,province_or_state,country]
Set-Grouping concept hierarchy
 define hierarchy age_hierarchy for age on customer as
level1: {young, middle_aged, senior} < level0: all
level2: {20, ..., 39} < level1: young
level2: {40, ..., 59} < level1: middle_aged
level2: {60, ..., 89} < level1: senior
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Syntax for concept hierarchy specification (2)
operation-derived concept hierarchy
 define hierarchy age_hierarchy for age on customer as
{age_category(1), ..., age_category(5)} := cluster (default, age, 5)
< all(age)
rule-based concept hierarchy
 define hierarchy profit_margin_hierarchy on item as
level_1: low_profit_margin < level_0: all
if (price - cost)< $50
level_1: medium-profit_margin < level_0: all
if ((price - cost) > $50) and ((price - cost) <= $250))
level_1: high_profit_margin < level_0: all
if (price - cost) > $250
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Syntax for interestingness measure
specification




with [‹interest_measure_name›] threshold =
‹threshold_value›
Example:
with support threshold = 5%
with confidence threshold = 70%
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Syntax for pattern presentation and
visualization specification




display as ‹result_form›
The result form can be rules, tables, cubes, crosstabs, pie or
bar charts, decision trees, curves or surfaces.
To facilitate interactive viewing at different concept levels or
different angles, the following syntax is defined:
‹Multilevel_Manipulation› ::= roll up on
|
|
|
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‹attribute_or_dimension›
drill down on ‹attribute_or_dimension›
add ‹attribute_or_dimension›
drop ‹attribute_or_dimension›
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Architectures of Data Mining System




With popular and diverse application of data mining, it is
expected that a good variety of data mining system will
be designed and developed.
Comprehensive information processing and data analysis
will be continuously and systematically surrounded by
data warehouse and databases.
A critical question in design is whether we should
integrate data mining systems with database systems.
This gives rise to four architecture:
No coupling
Loose Coupling
Semi-tight Coupling
Tight Coupling
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Cont.




No Coupling:
DB or DW system
DM system will not utilize any functionality of a
Loose Coupling: DM system will use some facilities of DB and
DW system like storing the data in either of DB or DW systems and
using these systems for data retrieval
Semi-tight Coupling: Besides linking a DM system to a DB/DW
systems, efficient implementation of a few DM primitives.
Tight Coupling: DM system is smoothly integrated with DB/DW
systems. Each of these DM, DB/DW is treated as main functional
component of information retrieval system.
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Designing Graphical User Interfaces based on a
data mining query language

What tasks should be considered in the design GUIs
based on a data mining query language?

Data collection and data mining query composition

Presentation of discovered patterns

Hierarchy specification and manipulation

Manipulation of data mining primitives

Interactive multilevel mining

Other miscellaneous information
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Summary



Five primitives for specification of a data mining task
 task-relevant data
 kind of knowledge to be mined
 background knowledge
 interestingness measures
 knowledge presentation and visualization techniques
to be used for displaying the discovered patterns
Data mining query languages
 DMQL, MS/OLEDB for DM, etc.
Data mining system architecture
 No coupling, loose coupling, semi-tight coupling, tight
coupling
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Measurements of Pattern Interestingness




Simplicity
e.g., (association) rule length, (decision) tree size
Certainty
e.g., confidence, P(A|B) = #(A and B)/ #(B),
classification reliability or accuracy, certainty factor,
rule strength, rule quality, discriminating weight, etc.
Utility
potential usefulness, e.g., support (association), noise
threshold (description)
Novelty
not previously known, surprising (used to remove
redundant rules, e.g., Canada vs. Vancouver rule
implication support ratio)
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Visualization of Discovered Patterns

Different backgrounds/usages may require different forms
of representation


Concept hierarchy is also important



E.g., rules, tables, crosstabs, pie/bar chart etc.
Discovered knowledge might be more understandable
when represented at high level of abstraction
Interactive drill up/down, pivoting, slicing and dicing
provide different perspectives to data
Different kinds of knowledge require different
representation: association, classification, clustering, etc.
March 22, 2016
Data Mining: Concepts and Techniques
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Chapter 4: Data Mining Primitives, Languages, and
System Architectures

Data mining primitives: What defines a data
mining task?

A data mining query language

Design graphical user interfaces based on a
data mining query language

Architecture of data mining systems

Summary
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Data Mining: Concepts and Techniques
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A Data Mining Query Language (DMQL)

Motivation


A DMQL can provide the ability to support ad-hoc and
interactive data mining
By providing a standardized language like SQL




Hope to achieve a similar effect like that SQL has on
relational database
Foundation for system development and evolution
Facilitate information exchange, technology transfer,
commercialization and wide acceptance
Design

DMQL is designed with the primitives described earlier
March 22, 2016
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Syntax for DMQL


Syntax for specification of

task-relevant data

the kind of knowledge to be mined

concept hierarchy specification

interestingness measure

pattern presentation and visualization
Putting it all together—a DMQL query
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Syntax: Specification of Task-Relevant Data

use database database_name, or use data
warehouse data_warehouse_name

from relation(s)/cube(s) [where condition]

in relevance to att_or_dim_list

order by order_list

group by grouping_list

having condition
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Specification of task-relevant data
March 22, 2016
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Syntax: Kind of knowledge to Be Mined

Characterization
Mine_Knowledge_Specification ::=
mine characteristics [as pattern_name]
analyze measure(s)

Discrimination
Mine_Knowledge_Specification ::=
mine comparison [as pattern_name]
for target_class where target_condition
{versus contrast_class_i where contrast_condition_i}
analyze measure(s)
E.g. mine comparison as purchaseGroups
for bigSpenders where avg(I.price) >= $100
versus budgetSpenders where avg(I.price) < $100
analyze count
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Syntax: Kind of Knowledge to Be Mined (cont.)

Association
Mine_Knowledge_Specification ::=
mine associations [as pattern_name]
[matching <metapattern>]
E.g. mine associations as buyingHabits
matching P(X:custom, W) ^ Q(X, Y)=>buys(X, Z)

Classification
Mine_Knowledge_Specification ::=
mine classification [as pattern_name]
analyze classifying_attribute_or_dimension

Other Patterns
clustering, outlier analysis, prediction …
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Syntax: Concept Hierarchy Specification

To specify what concept hierarchies to use
use hierarchy <hierarchy> for <attribute_or_dimension>

We use different syntax to define different type of hierarchies

schema hierarchies
define hierarchy time_hierarchy on date as [date,month
quarter,year]

set-grouping hierarchies
define hierarchy age_hierarchy for age on customer as
level1: {young, middle_aged, senior} < level0: all
level2: {20, ..., 39} < level1: young
level2: {40, ..., 59} < level1: middle_aged
level2: {60, ..., 89} < level1: senior
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Concept Hierarchy Specification (Cont.)

operation-derived hierarchies
define hierarchy age_hierarchy for age on customer as
{age_category(1), ..., age_category(5)} :=
cluster(default, age, 5) < all(age)

rule-based hierarchies
define hierarchy profit_margin_hierarchy on item as
level_1: low_profit_margin < level_0: all
if (price - cost)< $50
level_1: medium-profit_margin < level_0: all
if ((price - cost) > $50) and ((price - cost) <= $250))
level_1: high_profit_margin < level_0: all
if (price - cost) > $250
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Specification of Interestingness Measures

Interestingness measures and thresholds can be
specified by a user with the statement:
with <interest_measure_name> threshold =
threshold_value

Example:
with support threshold = 0.05
with confidence threshold = 0.7
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Specification of Pattern Presentation

Specify the display of discovered patterns
display as <result_form>

To facilitate interactive viewing at different concept
level, the following syntax is defined:
Multilevel_Manipulation ::= roll up on attribute_or_dimension
| drill down on attribute_or_dimension
| add attribute_or_dimension
| drop attribute_or_dimension
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Putting it all together: A DMQL query
use database AllElectronics_db
use hierarchy location_hierarchy for B.address
mine characteristics as customerPurchasing
analyze count%
in relevance to C.age, I.type, I.place_made
from customer C, item I, purchases P, items_sold S,
works_at W, branch
where I.item_ID = S.item_ID and S.trans_ID = P.trans_ID
and P.cust_ID = C.cust_ID and P.method_paid =
``AmEx''
and P.empl_ID = W.empl_ID and W.branch_ID =
B.branch_ID and B.address = ``Canada" and I.price
>= 100
with noise threshold = 0.05
display as table
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Other Data Mining Languages &
Standardization Efforts



Association rule language specifications

MSQL (Imielinski & Virmani’99)

MineRule (Meo Psaila and Ceri’96)

Query flocks based on Datalog syntax (Tsur et al’98)
OLEDB for DM (Microsoft’2000)

Based on OLE, OLE DB, OLE DB for OLAP

Integrating DBMS, data warehouse and data mining
CRISP-DM (CRoss-Industry Standard Process for Data Mining)

Providing a platform and process structure for effective data mining

Emphasizing on deploying data mining technology to solve business
problems
March 22, 2016
Data Mining: Concepts and Techniques
69
Chapter 4: Data Mining Primitives, Languages,
and System Architectures

Data mining primitives: What defines a data
mining task?

A data mining query language

Design graphical user interfaces based on a
data mining query language

Architecture of data mining systems

Summary
March 22, 2016
Data Mining: Concepts and Techniques
70
Designing Graphical User Interfaces
Based on a Data Mining Query Language

What tasks should be considered in the design GUIs
based on a data mining query language?

Data collection and data mining query composition

Presentation of discovered patterns

Hierarchy specification and manipulation

Manipulation of data mining primitives

Interactive multilevel mining

Other miscellaneous information
March 22, 2016
Data Mining: Concepts and Techniques
71
Chapter 4: Data Mining Primitives,
Languages, and System Architectures

Data mining primitives: What defines a data
mining task?

A data mining query language

Design graphical user interfaces based on a
data mining query language

Architecture of data mining systems

Summary
March 22, 2016
Data Mining: Concepts and Techniques
72
Data Mining System Architectures

Coupling data mining system with DB/DW system
 No coupling—flat file processing, not recommended
 Loose coupling


Semi-tight coupling—enhanced DM performance


Fetching data from DB/DW
Provide efficient implement a few data mining primitives in a
DB/DW system, e.g., sorting, indexing, aggregation, histogram
analysis, multiway join, precomputation of some stat functions
Tight coupling—A uniform information processing
environment

March 22, 2016
DM is smoothly integrated into a DB/DW system, mining query
is optimized based on mining query, indexing, query
processing methods, etc.
Data Mining: Concepts and Techniques
73
Chapter 4: Data Mining Primitives, Languages, and
System Architectures

Data mining primitives: What defines a data
mining task?

A data mining query language

Design graphical user interfaces based on a
data mining query language

Architecture of data mining systems

Summary
March 22, 2016
Data Mining: Concepts and Techniques
74
Summary



Five primitives for specification of a data mining task
 task-relevant data
 kind of knowledge to be mined
 background knowledge
 interestingness measures
 knowledge presentation and visualization techniques
to be used for displaying the discovered patterns
Data mining query languages
 DMQL, MS/OLEDB for DM, etc.
Data mining system architecture
 No coupling, loose coupling, semi-tight coupling,
tight coupling
March 22, 2016
Data Mining: Concepts and Techniques
75
References









E. Baralis and G. Psaila. Designing templates for mining association rules. Journal of Intelligent
Information Systems, 9:7-32, 1997.
Microsoft Corp., OLEDB for Data Mining, version 1.0, http://www.microsoft.com/data/oledb/dm,
Aug. 2000.
J. Han, Y. Fu, W. Wang, K. Koperski, and O. R. Zaiane, “DMQL: A Data Mining Query Language for
Relational Databases”, DMKD'96, Montreal, Canada, June 1996.
T. Imielinski and A. Virmani. MSQL: A query language for database mining. Data Mining and
Knowledge Discovery, 3:373-408, 1999.
M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A.I. Verkamo. Finding interesting rules
from large sets of discovered association rules. CIKM’94, Gaithersburg, Maryland, Nov. 1994.
R. Meo, G. Psaila, and S. Ceri. A new SQL-like operator for mining association rules. VLDB'96,
pages 122-133, Bombay, India, Sept. 1996.
A. Silberschatz and A. Tuzhilin. What makes patterns interesting in knowledge discovery systems.
IEEE Trans. on Knowledge and Data Engineering, 8:970-974, Dec. 1996.
S. Sarawagi, S. Thomas, and R. Agrawal. Integrating association rule mining with relational
database systems: Alternatives and implications. SIGMOD'98, Seattle, Washington, June 1998.
D. Tsur, J. D. Ullman, S. Abitboul, C. Clifton, R. Motwani, and S. Nestorov. Query flocks: A
generalization of association-rule mining. SIGMOD'98, Seattle, Washington, June 1998.
March 22, 2016
Data Mining: Concepts and Techniques
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www.cs.uiuc.edu/~hanj
Thank you !!!
March 22, 2016
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