Clustering
Lecture 1: Basics
Jing Gao
SUNY Buffalo
1
Class Structure
• Topics
– Clustering, Classification
– Network mining
– Anomaly detection
• Expectation
– Sign-in
– Take quiz in class
– Two more projects on clustering and classification
– One more homework on network mining or anomaly detection
• Website
– http://www.cse.buffalo.edu/~jing/cse601/fa12/
2
Outline
• Basics
– Motivation, definition, evaluation
• Methods
–
–
–
–
–
Partitional,
Hierarchical
Density-based
Mixture model
Spectral methods
• Advanced topics
– Clustering ensemble
– Clustering in MapReduce
– Semi-supervised clustering, subspace clustering, co-clustering,
etc.
3
Readings
• Tan, Steinbach, Kumar, Chapters 8 and 9.
• Han, Kamber, Pei. Data Mining: Concepts and Techniques.
Chapters 10 and 11.
• Additional readings posted on website
4
Clustering Basics
•
•
•
•
Definition and Motivation
Data Preprocessing and Similarity Computation
Objective of Clustering
Clustering Evaluation
5
Clustering
• Finding groups of objects such that the objects in a group will
be similar (or related) to one another and different from (or
unrelated to) the objects in other groups
Intra-cluster
distances are
minimized
Inter-cluster
distances are
maximized
6
Application Examples
• A stand-alone tool: explore data distribution
• A preprocessing step for other algorithms
• Pattern recognition, spatial data analysis, image processing,
market research, WWW, …
– Cluster documents
– Cluster web log data to discover groups of similar access
patterns
7
Clustering Co-expressed Genes
Gene Expression Data Matrix
Gene Expression Patterns
Co-expressed Genes
Why looking for co-expressed genes?
 Co-expression indicates co-function;
 Co-expression also indicates co-regulation.
8
Gene-based Clustering
1.5
Expression Value
1
0.5
0
-0.5
-1
-1.5
Time Point
1.5
Expression Level
1
0.5
0
-0.5
-1
-1.5
Time Points
1.5
Expression Value
1
0.5
0
-0.5
-1
-1.5
Time Points
Examples of co-expressed genes and coherent
patterns in gene expression data
Iyer’s data [2]
 [2] Iyer, V.R. et al. The transcriptional program in the response of human fibroblasts to serum. Science,
283:83–87, 1999.
9
Other Applications
• Land use: Identification of areas of similar land use in an earth
observation database
• Marketing: Help marketers discover distinct groups in their
customer bases, and then use this knowledge to develop
targeted marketing programs
• City-planning: Identifying groups of houses according to their
house type, value, and geographical location
• Climate: understanding earth climate, find patterns of
atmospheric and ocean
10
Two Important Aspects
• Properties of input data
– Define the similarity or dissimilarity between points
• Requirement of clustering
– Define the objective and methodology
11
Clustering Basics
•
•
•
•
Definition and Motivation
Data Preprocessing and Distance computation
Objective of Clustering
Clustering Evaluation
12
Data Representation
•
Data: Collection of data objects
and their attributes
•
An attribute is a property or
characteristic of an object
Tid Refund Marital
Status
Taxable
Income Cheat
1
Yes
Single
125K
No
2
No
Married
100K
No
3
No
Single
70K
No
4
Yes
Married
120K
No
5
No
Divorced 95K
Yes
6
No
Married
No
A collection of attributes describe
an object
7
Yes
Divorced 220K
No
8
No
Single
85K
Yes
– Object is also known as record,
point, case, sample, entity, or
instance
9
No
Married
75K
No
10
No
Single
90K
Yes
– Examples: eye color of a person,
temperature, etc.
– Attribute is also known as
dimension, variable, field,
characteristic, or feature
•
Attributes
Objects
60K
10
13
Data Matrix
• Represents n objects with p variables
– An n by p matrix
The value of the i-th
object on the f-th
attribute
Attributes
Objects
 x11  x1 f



 
x
 x
if
 i1
 



 xn1  xnf
x 
1p 

 
 x 
ip 

 

 x 
np 

14
Gene Expression Data
sample 1 sample 2 sample 3 sample 4 sample …
•
Clustering genes
gene 1
0.13
0.72
0.1
0.57
•Genes are objects
gene 2
0.34
1.58
1.05
1.15
gene 3
0.43
1.1
0.97
1
•Experiment conditions are
attributes
gene 4
1.22
0.97
1
0.85
gene 5
-0.89
1.21
1.29
1.08
gene 6
1.1
1.45
1.44
1.12
gene 7
0.83
1.15
1.1
1
gene 8
0.87
1.32
1.35
1.13
gene 9
-0.33
1.01
1.38
1.21
gene 10
0.10
0.85
1.03
1
• Find genes with similar
function
gene
…
15
Similarity and Dissimilarity
• Similarity
– Numerical measure of how alike two data objects are
– Is higher when objects are more alike
– Often falls in the range [0,1]
• Dissimilarity
– Numerical measure of how different are two data
objects
– Lower when objects are more alike
– Minimum dissimilarity is often 0
– Upper limit varies
16
Distance Matrix
• Represents pairwise distance in n objects
–
–
–
–
An n by n matrix
d(i,j): distance or dissimilarity between objects i and j
Nonnegative
Close to 0: similar
 0

d (2,1)

0


d (3,1) d (3,2) 0








d (n,1) d (n,2)   0
17
Data Matrix -> Distance Matrix
s1
g1
s2
s3
…
s4
0.13
0.72
0.1 0.57
g2
0.34
1.58 1.05 1.15
g3
0.43
g4
1.22
g5
-0.89
1.21 1.29 1.08
g6
1.1
1.45 1.44 1.12
g3
g7
0.83
1.15
g4
g8
0.87
1.32 1.35 1.13
g9
-0.33
1.01 1.38 1.21
g 10
0.10
1
g1
1 0.85
g2
1.1 0.97
0.97
g1
1.1
0.85 1.03
1
0
g2
g3
g4
…
d(1,2) d(1,3) d(1,4)
0
d(2,3) d(2,4)
0
d(3,4)
0
…
1
Distance Matrix
…
Original Data Matrix
18
Types of Attributes
• Discrete
– Has only a finite or countably infinite set of values
– Examples: zip codes, counts, or the set of words in a collection of
documents
– Note: binary attributes are a special case of discrete attributes
• Ordinal
– Has only a finite or countably infinite set of values
– Order of values is important
– Examples: rankings (e.g., pain level 1-10), grades (A, B, C, D)
• Continuous
– Has real numbers as attribute values
– Examples: temperature, height, or weight
– Continuous attributes are typically represented as floating-point
variables
19
Similarity/Dissimilarity for Simple Attributes
p and q are the attribute values for two data objects.
Discrete
Ordinal
Continuous
Dissimilarity and similarity between p and q
20
Minkowski Distance—Continuous Attribute
• Minkowski distance: a generalization
d (i, j)  q | x  x |q  | x  x |q ... | x  x |q (q  0)
i1
j1
i2
j2
ip
jp
• If q = 2, d is Euclidean distance
• If q = 1, d is Manhattan distance
xi
Xi (1,7)
12
8.48
q=2
6
q=1
6
Xj(7,1)
xj
21
Standardization
• Calculate the mean absolute deviation
mf  1
n (x1 f  x2 f
 ... 
xnf )
.
sf  1
n (| x1 f  m f |  | x2 f  m f | ... | xnf  m f |)
• Calculate the standardized measurement (z-score)
xif  m f
zif 
sf
22
Mahalanobis Distance
1
d ( p, q)  ( p  q)  ( p  q)
T
 is the covariance matrix of the
input data X
 j ,k
1 n

 ( X ij  X j )( X ik  X k )
n  1 i 1
Belongs to the family of bregman
divergence
For red points, the Euclidean distance is 14.7, Mahalanobis distance is 6.
23
Mahalanobis Distance
Covariance Matrix:
C
0.3 0.2


0
.
2
0
.
3


A: (0.5, 0.5)
B
B: (0, 1)
A
C: (1.5, 1.5)
Mahal(A,B) = 5
Mahal(A,C) = 4
24
Common Properties of a Distance
•
Distances, such as the Euclidean distance, have
some well known properties
1.
2.
3.
d(p, q)  0 for all p and q and d(p, q) = 0 only if
p = q. (Positive definiteness)
d(p, q) = d(q, p) for all p and q. (Symmetry)
d(p, r)  d(p, q) + d(q, r) for all points p, q, and r.
(Triangle Inequality)
where d(p, q) is the distance (dissimilarity) between points
(data objects), p and q.
•
A distance that satisfies these properties is a
metric
25
Similarity for Binary Attributes
•
Common situation is that objects, p and q, have only
binary attributes
•
Compute similarities using the following quantities
M01 = the number of attributes where p was 0 and q was 1
M10 = the number of attributes where p was 1 and q was 0
M00 = the number of attributes where p was 0 and q was 0
M11 = the number of attributes where p was 1 and q was 1
•
Simple Matching and Jaccard Coefficients
SMC = number of matches / total number of attributes
= (M11 + M00) / (M01 + M10 + M11 + M00)
J = number of matches / number of not-both-zero attributes values
= (M11) / (M01 + M10 + M11)
26
SMC versus Jaccard: Example
p= 1000000000
q= 0000001001
M01 = 2
M10 = 1
M00 = 7
M11 = 0
(the number of attributes where p was 0 and q was 1)
(the number of attributes where p was 1 and q was 0)
(the number of attributes where p was 0 and q was 0)
(the number of attributes where p was 1 and q was 1)
SMC = (M11 + M00)/(M01 + M10 + M11 + M00) = (0+7) / (2+1+0+7) = 0.7
J = (M11) / (M01 + M10 + M11) = 0 / (2 + 1 + 0) = 0
27
Document Data
• Each document becomes a `term' vector,
– each term is a component (attribute) of the vector,
– the value of each component is the number of times the
corresponding term occurs in the document.
team
coach
pla
y
ball
score
game
wi
n
lost
timeout
season
Document 1
3
0
5
0
2
6
0
2
0
2
Document 2
0
7
0
2
1
0
0
3
0
0
Document 3
0
1
0
0
1
2
2
0
3
0
28
Cosine Similarity
• If d1 and d2 are two document vectors, then
cos( d1, d2 ) = (d1  d2) / ||d1|| ||d2|| ,
where  indicates vector dot product and || d || is the length of vector d.
• Example:
d1 = 3 2 0 5 0 0 0 2 0 0
d2 = 1 0 0 0 0 0 0 1 0 2
d1  d2= 3*1 + 2*0 + 0*0 + 5*0 + 0*0 + 0*0 + 0*0 + 2*1 + 0*0 + 0*2 = 5
||d1|| = (3*3+2*2+0*0+5*5+0*0+0*0+0*0+2*2+0*0+0*0)0.5 = (42) 0.5 = 6.481
||d2|| = (1*1+0*0+0*0+0*0+0*0+0*0+0*0+1*1+0*0+2*2) 0.5 = (6) 0.5 = 2.245
cos( d1, d2 ) = .3150
29
Correlation
• Correlation measures the linear relationship between objects
• To compute correlation, we standardize data objects, p and q,
and then take their dot product (continuous attributes)
pk  ( pk  mean( p)) / std ( p)
qk  (qk  mean(q)) / std (q)
s( p, q)  p  q
30
Common Properties of a Similarity
• Similarities, also have some well known
properties.
1.
s(p, q) = 1 (or maximum similarity) only if p = q.
2.
s(p, q) = s(q, p) for all p and q. (Symmetry)
where s(p, q) is the similarity between points (data
objects), p and q.
31
Characteristics of the Input Data Are Important
•
•
•
•
•
•
Sparseness
Attribute type
Type of Data
Dimensionality
Noise and Outliers
Type of Distribution
• => Conduct preprocessing and select the appropriate
dissimilarity or similarity measure
• => Determine the objective of clustering and choose
the appropriate method
32
Clustering Basics
•
•
•
•
Definition and Motivation
Data Preprocessing and Distance computation
Objective of Clustering
Clustering Evaluation
33
Considerations for Cluster Analysis
• Partitioning criteria
– Single level vs. hierarchical partitioning (often, multi-level hierarchical
partitioning is desirable)
• Separation of clusters
– Exclusive (e.g., one customer belongs to only one region) vs. overlapping
(e.g., one document may belong to more than one topic)
• Hard versus fuzzy
– In fuzzy clustering, a point belongs to every cluster with some weight
between 0 and 1
– Weights must sum to 1
– Probabilistic clustering has similar characteristics
• Similarity measure and data types
• Heterogeneous versus homogeneous
– Cluster of widely different sizes, shapes, and densities
34
Requirements of Clustering
• Scalability
• Ability to deal with different types of attributes
• Minimal requirements for domain knowledge to determine
input parameters
• Able to deal with noise and outliers
• Discovery of clusters with arbitrary shape
• Insensitive to order of input records
• High dimensionality
• Incorporation of user-specified constraints
• Interpretability and usability
• What clustering results we want to get?
35
Notion of a Cluster can be Ambiguous
How many clusters?
Six Clusters
Two Clusters
Four Clusters
36
Partitional Clustering
Input Data
A Partitional Clustering
37
Hierarchical Clustering
p1
p3
p4
p2
p1 p2
p3 p4
Clustering Solution 1
p1
p3
p4
p2
p1 p2
p3 p4
Clustering Solution 2
38
Types of Clusters: Center-Based
• Center-based
– A cluster is a set of objects such that an object in a cluster is closer
(more similar) to the “center” of a cluster, than to the center of any
other cluster
– The center of a cluster is often a centroid, the average of all the
points in the cluster, or a medoid, the most “representative” point
of a cluster
4 center-based clusters
39
Types of Clusters: Density-Based
• Density-based
– A cluster is a dense region of points, which is separated by lowdensity regions, from other regions of high density.
– Used when the clusters are irregular or intertwined, and when noise
and outliers are present.
6 density-based clusters
40
Clustering Basics
•
•
•
•
Definition and Motivation
Data Preprocessing and Distance computation
Objective of Clustering
Clustering Evaluation
41
Cluster Validation
• Cluster validation
– Quality: “goodness” of clusters
– Assess the quality and reliability of clustering
results
• Why validation?
– To avoid finding clusters formed by chance
– To compare clustering algorithms
– To choose clustering parameters
• e.g., the number of clusters
42
Aspects of Cluster Validation
•
Comparing the clustering results to ground truth
(externally known results)
– External Index
•
Evaluating the quality of clusters without reference
to external information
– Use only the data
– Internal Index
•
Determining the reliability of clusters
– To what confidence level, the clusters are not formed
by chance
– Statistical framework
43
Comparing to Ground Truth
• Notation
– N: number of objects in the data set
– P={P1,…,Ps}: the set of “ground truth” clusters
– C={C1,…,Ct}: the set of clusters reported by a clustering
algorithm
• The “incidence matrix”
– N  N (both rows and columns correspond to objects)
– Pij = 1 if Oi and Oj belong to the same “ground truth” cluster
in P; Pij=0 otherwise
– Cij = 1 if Oi and Oj belong to the same cluster in C; Cij=0
otherwise
44
Rand Index and Jaccard Coefficient
• A pair of data object (Oi,Oj) falls into one of the
following categories
–
–
–
–
SS: Cij=1 and Pij=1;
DD: Cij=0 and Pij=0;
SD: Cij=1 and Pij=0;
DS: Cij=0 and Pij=1;
• Rand index
Rand 
(agree)
(agree)
(disagree)
(disagree)
| Agree |
| SS |  | DD |

| Agree |  | Disagree | | SS |  | SD |  | DS |  | DD |
– may be dominated by DD
• Jaccard Coefficient
| SS |
Jaccard coefficien t 
| SS |  | SD |  | DS |
45
Clustering
g1
g2
g3
g4
g5
g1
1
1
1
0
0
g2
1
1
1
0
0
Clustering
g3
1
1
1
0
0
Same
Cluster
Different
Cluster
g4
0
0
0
1
1
9
4
g5
0
0
0
1
1
4
8
g2
g3
g4
g5
Ground
truth
Same
Cluster
Different
Cluster
Groundtruth
g1
g1
1
1
0
0
0
g2
1
1
0
0
0
g3
0
0
1
1
1
g4
0
0
1
1
1
g5
0
0
1
1
1
Rand 
| SS |  | DD |
17

| SS |  | SD |  | DS |  | DD | 25
Jaccard 
| SS |
9

| SS |  | SD |  | DS | 17
46
Entropy and Purity
• Notation
– | Ck  Pj | the number of objects in both the k-th cluster of
the clustering solution and j-th cluster of the groundtruth
– | Ck | the number of objects in the k-th cluster of the
clustering solution
– | Pj | the number of objects in the j-th cluster of the
groundtruth
• Purity
Purity 
1
max j | Ck  Pj |

N k
• Normalized Mutual Information
NMI 
I (C , P)
H (C ) H ( P)
I (C , P)  
k
H (C )  
k
j
| Ck  Pj |
N
| Ck |
|C |
log k
N
N
log
N  | Ck  Pj |
| Ck || Pj |
H ( P)  
j
| Pj |
N
log
| Pj |
N
47
Example
P1
P2
P3
P4
P5
P6
Total
C1
3
5
40
506
96
27
677
C2
4
7
280
29
39
2
361
C3
1
1
1
7
4
671
685
C4
10
162
3
119
73
2
369
C5
331
22
5
70
13
23
464
C6
5
358
12
212
48
13
648
total
354
555
341
943
273
738
3204
NMI 
I (C , P)
H (C ) H ( P)
I (C , P)  
k
H (C )  
k
j
Purity 
1
 max j | Ck  Pj |
N k
506  280  671  162  331  358
3204
 0.7203
Purity 
| Ck  Pj |
N
| Ck |
|C |
log k
N
N
log
N  | Ck  Pj |
| Ck || Pj |
H ( P)  
j
| Pj |
N
log
| Pj |
N
48
Internal Index
• “Ground truth” may be unavailable
• Use only the data to measure cluster quality
– Measure the “cohesion” and “separation” of clusters
– Calculate the correlation between clustering results
and distance matrix
49
Cohesion and Separation
• Cohesion is measured by the within cluster sum of squares
WSS    ( x  mi ) 2
i
xCi
• Separation is measured by the between cluster sum of squares
BSS   Ci (m  mi )2
i
where |Ci| is the size of cluster i, m is the centroid of the whole data set
• BSS + WSS = constant
• WSS (Cohesion) measure is called Sum of Squared Error (SSE)—a
commonly used measure
• A larger number of clusters tend to result in smaller SSE
50
Example
1

m1
m
2

3
4

m2
5
WSS (1  3)2  (2  3)2  (4  3)2  (5  3)2  10
K=1 :
BSS  4  (3  3)2  0
Total  10  0  10
K=2 :
WSS (1  1.5) 2  (2  1.5) 2  (4  4.5) 2  (5  4.5) 2  1
BSS  2  (3  1.5) 2  2  (4.5  3) 2  9
Total  1  9  10
K=4:
WSS (1  1) 2  (2  2) 2  (4  4) 2  (5  5) 2  0
BSS  1 (1  3) 2  1 (2  3) 2  1 (4  3) 2  1 (5  3) 2  10
Total  0  10  10
51
Silhouette Coefficient
•
Silhouette Coefficient combines ideas of both cohesion and separation
•
For an individual point, i
– Calculate a = average distance of i to the points in its cluster
– Calculate b = min (average distance of i to points in another cluster)
– The silhouette coefficient for a point is then given by
s = 1 – a/b if a < b,
(s = b/a - 1 if a  b, not the usual case)
– Typically between 0 and 1
– The closer to 1 the better
b
a
• Can calculate the Average Silhouette width for a cluster or a clustering
52
Correlation with Distance Matrix
• Distance Matrix
– Dij is the similarity between object Oi and Oj
• Incidence Matrix
– Cij=1 if Oi and Oj belong to the same cluster, Cij=0
otherwise
• Compute the correlation between the two
matrices
– Only n(n-1)/2 entries needs to be calculated
• High correlation indicates good clustering
53
Correlation with Distance Matrix
• Given Distance Matrix D = {d11,d12, …, dnn } and Incidence
Matrix C= { c11, c12,…, cnn } .
• Correlation r between D and C is given by
n
r
 (d
i 1, j 1
n
_
ij
_
 d )(cij  c)
 (dij  d )
i 1, j 1
_
n
2
_
2
(
c

c
)
 ij
i 1, j 1
54
1
1
0.9
0.9
0.8
0.8
0.7
0.7
0.6
0.6
0.5
0.5
y
Random
Points
y
Are There Clusters in the Data?
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
0
0.2
0.4
0.6
0.8
0
1
DBSCAN
0
0.2
0.4
x
1
1
0.9
0.9
0.8
0.8
0.7
0.7
0.6
0.6
0.5
0.5
y
y
K-means
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
0
0.2
0.4
0.6
x
0.6
0.8
1
x
0.8
1
0
Complete
Link
0
0.2
0.4
0.6
0.8
1
x
55
Measuring Cluster Validity Via Correlation
1
1
0.9
0.9
0.8
0.8
0.7
0.7
0.6
0.6
0.5
0.5
y
y
• Correlation of incidence and distance matrices for the Kmeans clusterings of the following two data sets
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
0
0.2
0.4
0.6
x
Corr = -0.9235
0.8
1
0
0
0.2
0.4
0.6
0.8
1
x
Corr = -0.5810
56
Using Similarity Matrix for Cluster Validation
• Order the similarity matrix with respect to cluster
labels and inspect visually.
1
1
0.9
0.8
0.7
Points
y
0.6
0.5
0.4
0.3
0.2
0.1
0
10
0.9
20
0.8
30
0.7
40
0.6
50
0.5
60
0.4
70
0.3
80
0.2
90
0.1
100
0
0.2
0.4
0.6
x
0.8
1
20
40
60
80
0
100 Similarity
Points
57
Using Similarity Matrix for Cluster Validation
• Clusters in random data are not so crisp
1
1
10
0.9
20
0.8
30
0.7
40
0.6
50
0.5
60
0.4
70
0.3
80
0.2
90
0.1
0.9
0.8
0.7
Points
y
0.6
0.5
0.4
0.3
0.2
0.1
0
100
0
0.2
0.4
0.6
x
0.8
1
20
40
60
80
0
100 Similarity
Points
58
Reliability of Clusters
• Need a framework to interpret any measure
– For example, if our measure of evaluation has the
value, 10, is that good, fair, or poor?
• Statistics provide a framework for cluster validity
– The more “atypical” a clustering result is, the more
likely it represents valid structure in the data
59
Statistical Framework for SSE
• Example
– Compare SSE of 0.005 against three clusters in random data
– SSE Histogram of 500 sets of random data points of size 100—
lowest SSE is 0.0173
1
50
0.9
45
0.8
40
0.7
35
30
Count
y
0.6
0.5
0.4
20
0.3
15
0.2
10
0.1
0
25
5
0
0.2
0.4
0.6
x
0.8
1
0
0.016 0.018
0.02
0.022
0.024
0.026
0.028
0.03
0.032
0.034
SSE
SSE = 0.005
60
Determine the Number of Clusters Using SSE
• SSE curve
10
9
6
8
4
7
6
SSE
2
0
5
4
-2
3
2
-4
1
-6
0
5
10
15
Clustering of Input Data
2
5
10
15
20
25
30
K
SSE wrt K
61
Take-away Message
• What’s clustering?
• Why clustering is important?
• How to preprocess data and compute
dissimilarity/similarity from data?
• What’s a good clustering solution?
• How to evaluate the clustering results?
62