Basic Clustering Algorithms in Data Warehouisng and Data Miningppt

Data Mining:
Concepts and Techniques
(3rd
ed.)
— Chapter 10 —
Jiawei Han, Micheline Kamber, and Jian Pei
University of Illinois at Urbana-Champaign &
Simon Fraser University
©2011 Han, Kamber & Pei. All rights reserved.
1

2
Chapter 10. Cluster Analysis: Basic Concepts and
Methods
 Cluster Analysis: Basic Concepts
 Partitioning Methods
 Hierarchical Methods
 Density-Based Methods
 Grid-Based Methods
 Evaluation of Clustering
 Summary
2

3
What is Cluster Analysis?
 Cluster: A collection of data objects
 similar (or related) to one another within the same group
 dissimilar (or unrelated) to the objects in other groups
 Cluster analysis (or clustering, data segmentation, …)
 Finding similarities between data according to the
characteristics found in the data and grouping similar
data objects into clusters
 Unsupervised learning: no predefined classes (i.e., learning
by observations vs. learning by examples: supervised)
 Typical applications
 As a stand-alone tool to get insight into data distribution
 As a preprocessing step for other algorithms

4
Clustering for Data Understanding and
Applications
 Biology: taxonomy of living things: kingdom, phylum, class, order,
family, genus and species
 Information retrieval: document clustering
 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
 Earth-quake studies: Observed earth quake epicenters should be
clustered along continent faults
 Climate: understanding earth climate, find patterns of atmospheric
and ocean
 Economic Science: market resarch

5
Clustering as a Preprocessing Tool (Utility)
 Summarization:
 Preprocessing for regression, PCA, classification, and
association analysis
 Compression:
 Image processing: vector quantization
 Finding K-nearest Neighbors
 Localizing search to one or a small number of clusters
 Outlier detection
 Outliers are often viewed as those “far away” from any
cluster

Quality: What Is Good Clustering?
 A good clustering method will produce high quality
clusters
 high intra-class similarity: cohesive within clusters
 low inter-class similarity: distinctive between clusters
 The quality of a clustering method depends on
 the similarity measure used by the method
 its implementation, and
 Its ability to discover some or all of the hidden patterns
6

Measure the Quality of Clustering
 Dissimilarity/Similarity metric
 Similarity is expressed in terms of a distance function,
typically metric: d(i, j)

The definitions of distance functions are usually rather
different for interval-scaled, boolean, categorical, ordinal
ratio, and vector variables
 Weights should be associated with different variables
based on applications and data semantics
 Quality of clustering:
 There is usually a separate “quality” function that
measures the “goodness” of a cluster.

It is hard to define “similar enough” or “good enough”

The answer is typically highly subjective
7

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.
non-exclusive (e.g., one document may belong to more than one
class)
 Similarity measure
 Distance-based (e.g., Euclidian, road network, vector) vs.
connectivity-based (e.g., density or contiguity)
 Clustering space
 Full space (often when low dimensional) vs. subspaces (often in
high-dimensional clustering)
8

Requirements and Challenges
 Scalability

Clustering all the data instead of only on samples
 Ability to deal with different types of attributes

Numerical, binary, categorical, ordinal, linked, and mixture of these
 Constraint-based clustering
 User may give inputs on constraints
 Use domain knowledge to determine input parameters
 Interpretability and usability
 Others
 Discovery of clusters with arbitrary shape

Ability to deal with noisy data
 Incremental clustering and insensitivity to input order

High dimensionality
9

Major Clustering Approaches (I)
 Partitioning approach:
 Construct various partitions and then evaluate them by some
criterion, e.g., minimizing the sum of square errors
 Typical methods: k-means, k-medoids, CLARANS
 Hierarchical approach:
 Create a hierarchical decomposition of the set of data (or objects)
using some criterion
 Typical methods: Diana, Agnes, BIRCH, CAMELEON
 Density-based approach:
 Based on connectivity and density functions
 Typical methods: DBSACN, OPTICS, DenClue
 Grid-based approach:
 based on a multiple-level granularity structure
 Typical methods: STING, WaveCluster, CLIQUE
10

Major Clustering Approaches (II)
 Model-based:
 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: p-Cluster
 User-guided or constraint-based:
 Clustering by considering user-specified or application-specific
constraints
 Typical methods: COD (obstacles), constrained clustering
 Link-based clustering:
 Objects are often linked together in various ways
 Massive links can be used to cluster objects: SimRank, LinkClus
11

12
Methods
 Summary
12

Partitioning Algorithms: Basic Concept
 Partitioning method: Partitioning a database D of n objects into a set of k
clusters, such that the sum of squared distances is minimized (where ci is
the centroid or medoid of cluster Ci)
 Given k, find a partition of k clusters that optimizes the chosen partitioning
criterion

Global optimal: exhaustively enumerate all partitions
 Heuristic methods: k-means and k-medoids algorithms
 k-means (MacQueen’67, Lloyd’57/’82): Each cluster is represented by
the center of the cluster
 k-medoids or PAM (Partition around medoids) (Kaufman &
Rousseeuw’87): Each cluster is represented by one of the objects in
the cluster
2
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k
i c
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

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The K-Means Clustering Method
 Given k, the k-means algorithm is implemented in four
steps:
 Partition objects into k nonempty subsets
 Compute seed points as the centroids of the
clusters of the current partitioning (the centroid is
the center, i.e., mean point, of the cluster)
 Assign each object to the cluster with the nearest
seed point
 Go back to Step 2, stop when the assignment does
not change
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An Example of K-Means Clustering
K=2
Arbitrarily
partition
objects
into k
groups
Update
the cluster
centroids
Update
the cluster
centroids
Reassign objects
Loop if
needed
15
The initial data
set
 Partition objects into k nonempty
subsets
 Repeat
 Compute centroid (i.e., mean
point) for each partition
 Assign each object to the
cluster of its nearest centroid
 Until no change

Comments on the K-Means Method
 Strength: Efficient: O(tkn), where n is # objects, k is # clusters, and t is #
iterations. Normally, k, t << n.

Comparing: PAM: O(k(n-k)2
), CLARA: O(ks2
+ k(n-k))
 Comment: Often terminates at a local optimal.
 Weakness

Applicable only to objects in a continuous n-dimensional space

Using the k-modes method for categorical data

In comparison, k-medoids can be applied to a wide range of data

Need to specify k, the number of clusters, in advance (there are
ways to automatically determine the best k (see Hastie et al., 2009)
 Sensitive to noisy data and outliers
 Not suitable to discover clusters with non-convex shapes
16

Variations of the K-Means Method
 Most of the variants of the k-means which differ in
 Selection of the initial k means
 Dissimilarity calculations
 Strategies to calculate cluster means
 Handling categorical data: k-modes
 Replacing means of clusters with modes
 Using new dissimilarity measures to deal with categorical objects
 Using a frequency-based method to update modes of clusters
 A mixture of categorical and numerical data: k-prototype method
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What Is the Problem of the K-Means Method?
 The k-means algorithm is sensitive to outliers !
 Since an object with an extremely large value may substantially
distort the distribution of the data
 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
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PAM: A Typical K-Medoids Algorithm
0
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Total Cost = 20
0
1
2
3
4
5
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7
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10
0 1 2 3 4 5 6 7 8 9 10
K=2
Arbitrar
y choose
k object
as initial
medoids
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10
Assign
each
remaini
ng
object to
nearest
medoids
Randomly select a
nonmedoid
object,Oramdom
Compute
total cost
of
swapping
0
1
2
3
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5
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Total Cost = 26
Swapping O
and Oramdom
If quality is
improved.
Do loop
Until no
change
0
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The K-Medoid Clustering Method
 K-Medoids Clustering: Find representative objects (medoids) in clusters

PAM (Partitioning Around Medoids, Kaufmann & Rousseeuw 1987)

Starts from an initial set of medoids and iteratively replaces one
of the medoids by one of the non-medoids if it improves the total
distance of the resulting clustering

PAM works effectively for small data sets, but does not scale well
for large data sets (due to the computational complexity)
 Efficiency improvement on PAM

CLARA (Kaufmann & Rousseeuw, 1990): PAM on samples
 CLARANS (Ng & Han, 1994): Randomized re-sampling
20

21
Methods
 Summary
21

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 Step 1 Step 2 Step 3 Step 4
b
d
c
e
a
a b
d e
c d e
a b c d e
Step 4 Step 3 Step 2 Step 1 Step 0
agglomerative
(AGNES)
divisive
(DIANA)
22

AGNES (Agglomerative Nesting)
 Introduced in Kaufmann and Rousseeuw (1990)
 Implemented in statistical packages, e.g., Splus
 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
0
1
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0 1 2 3 4 5 6 7 8 9 10
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Dendrogram: Shows How 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
24

DIANA (Divisive Analysis)
 Introduced in Kaufmann and Rousseeuw (1990)
 Implemented in statistical analysis packages, e.g., Splus
 Inverse order of AGNES
 Eventually each node forms a cluster on its own
0
1
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Distance between Clusters
 Single link: smallest distance between an element in one cluster and an
element in the other, i.e., dist(Ki, Kj) = min(tip, tjq)
 Complete link: largest distance between an element in one cluster and
an element in the other, i.e., dist(Ki, Kj) = max(tip, tjq)
 Average: avg distance between an element in one cluster and an
element in the other, i.e., dist(Ki, Kj) = avg(tip, tjq)
 Centroid: distance between the centroids of two clusters, i.e., dist(Ki, Kj)
= dist(Ci, Cj)
 Medoid: distance between the medoids of two clusters, i.e., dist(Ki, Kj) =
dist(Mi, Mj)
 Medoid: a chosen, centrally located object in the cluster
X X
26

Centroid, Radius and Diameter of a
Cluster (for numerical data sets)
 Centroid: the “middle” of a cluster
 Radius: square root of average distance from any point
of the cluster to its centroid
 Diameter: square root of average mean squared
distance between all pairs of points in the cluster
N
t
N
i ip
m
C
)
(
1



N
m
c
ip
t
N
i
m
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2
)
(
1
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)
1
(
2
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


N
N
iq
t
ip
t
N
i
N
i
m
D
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Extensions to Hierarchical Clustering
 Major weakness of agglomerative clustering methods
 Can never undo what was done previously
 Do not scale well: time complexity of at least O(n2
), where n
is the number of total objects
 Integration of hierarchical & distance-based clustering
 BIRCH (1996): uses CF-tree and incrementally adjusts the
quality of sub-clusters
 CHAMELEON (1999): hierarchical clustering using
dynamic modeling
28

BIRCH (Balanced Iterative Reducing and
Clustering Using Hierarchies)
 Zhang, Ramakrishnan & Livny, SIGMOD’96
 Incrementally construct a CF (Clustering Feature) tree, a hierarchical
data structure for multiphase clustering
 Phase 1: scan DB to build an initial in-memory CF tree (a multi-level
compression of the data that tries to preserve the inherent clustering
structure of the data)
 Phase 2: use an arbitrary clustering algorithm to cluster the leaf
nodes of the CF-tree
 Scales linearly: finds a good clustering with a single scan and improves
the quality with a few additional scans
 Weakness: handles only numeric data, and sensitive to the order of the
data record
29

Clustering Feature Vector in BIRCH
Clustering Feature (CF): CF = (N, LS, SS)
N: Number of data points
LS: linear sum of N points:
SS: square sum of N points
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10
CF = (5, (16,30),(54,190))
(3,4)
(2,6)
(4,5)
(4,7)
(3,8)


N
i
i
X
1
2
1


N
i
i
X
30

CF-Tree in BIRCH
 Clustering feature:

Summary of the statistics for a given subcluster: the 0-th, 1st,
and 2nd moments of the subcluster from the statistical point of
view

Registers crucial measurements for computing cluster and
utilizes storage efficiently
A CF tree is a height-balanced tree that stores the clustering
features for a hierarchical clustering

A nonleaf node in a tree has descendants or “children”

The nonleaf nodes store sums of the CFs of their children
 A CF tree has two parameters

Branching factor: max # of children

Threshold: max diameter of sub-clusters stored at the leaf
nodes
31

The CF Tree Structure
CF1
child1
CF3
child3
CF2
child2
CF6
child6
CF1
child1
CF3
child3
CF2
child2
CF5
child5
CF1 CF2 CF6
prev next CF1 CF2 CF4
prev next
B = 7
L = 6
Root
Non-leaf node
Leaf node Leaf node
32

The Birch Algorithm
 Cluster Diameter
 For each point in the input

Find closest leaf entry

Add point to leaf entry and update CF

If entry diameter > max_diameter, then split leaf, and possibly parents
 Algorithm is O(n)
 Concerns
 Sensitive to insertion order of data points

Since we fix the size of leaf nodes, so clusters may not be so natural
 Clusters tend to be spherical given the radius and diameter measures
 

2
)
(
)
1
(
1
j
x
i
x
n
n
33

CHAMELEON: Hierarchical Clustering Using
Dynamic Modeling (1999)
 CHAMELEON: G. Karypis, E. H. Han, and V. Kumar, 1999
 Measures the similarity based on a dynamic model

Two clusters are merged only if the interconnectivity and
closeness (proximity) between two clusters are high
relative to the internal interconnectivity of the clusters
and closeness of items within the clusters
 Graph-based, and a two-phase algorithm
1. Use a graph-partitioning algorithm: cluster objects into a
large number of relatively small sub-clusters
2. Use an agglomerative hierarchical clustering algorithm:
find the genuine clusters by repeatedly combining these
sub-clusters
34

Overall Framework of CHAMELEON
Construct (K-NN)
Sparse Graph Partition the Graph
Merge Partition
Final Clusters
Data Set
K-NN Graph
P and q are connected if
q is among the top k
closest neighbors of p
Relative interconnectivity:
connectivity of c1 and c2
over internal connectivity
Relative closeness:
closeness of c1 and c2 over
internal closeness 35

36
CHAMELEON (Clustering Complex Objects)

Probabilistic Hierarchical Clustering
 Algorithmic hierarchical clustering

Nontrivial to choose a good distance measure
 Hard to handle missing attribute values

Optimization goal not clear: heuristic, local search
 Probabilistic hierarchical clustering

Use probabilistic models to measure distances between clusters

Generative model: Regard the set of data objects to be clustered as
a sample of the underlying data generation mechanism to be
analyzed
 Easy to understand, same efficiency as algorithmic agglomerative
clustering method, can handle partially observed data
 In practice, assume the generative models adopt common distributions
functions, e.g., Gaussian distribution or Bernoulli distribution, governed
by parameters
37

Generative Model
 Given a set of 1-D points X = {x1, …, xn} for clustering
analysis & assuming they are generated by a
Gaussian distribution:
 The probability that a point xi ∈ X is generated by the
model
 The likelihood that X is generated by the model:
 The task of learning the generative model: find the
parameters μ and σ2
such that
the maximum
likelihood
38

A Probabilistic Hierarchical Clustering Algorithm
 For a set of objects partitioned into m clusters C1, . . . ,Cm, the quality can
be measured by,
where P() is the maximum likelihood
 Distance between clusters C1 and C2:
 Algorithm: Progressively merge points and clusters
Input: D = {o1, ..., on}: a data set containing n objects
Output: A hierarchy of clusters
Method
Create a cluster for each object Ci = {oi}, 1 ≤ i ≤ n;
For i = 1 to n {
Find pair of clusters Ci and Cj such that
Ci,Cj = argmaxi ≠ j {log (P(Ci C
∪ j )/(P(Ci)P(Cj ))};
If log (P(Ci C
∪ j )/(P(Ci)P(Cj )) > 0 then merge Ci and Cj }
39

40
Methods
 Summary
40

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)
41

Density-Based Clustering: Basic Concepts
 Two parameters:
 Eps: Maximum radius of the neighbourhood
 MinPts: Minimum number of points in an Eps-
neighbourhood of that point
 NEps(p): {q belongs to D | dist(p,q) ≤ Eps}
 Directly density-reachable: A point p is directly density-
reachable from a point q w.r.t. Eps, MinPts if

p belongs to NEps(q)
 core point condition:
|NEps (q)| ≥ MinPts
MinPts = 5
Eps = 1 cm
p
q
42

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
 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
p1
p q
o
43

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
Core
Border
Outlier
Eps = 1cm
MinPts = 5
44

DBSCAN: The Algorithm
 Arbitrary select a 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
 If p is a border point, no points are density-reachable
from p and DBSCAN visits the next point of the database
 Continue the process until all of the points have been
processed
45

DBSCAN: Sensitive to Parameters
46

OPTICS: A Cluster-Ordering Method (1999)
 OPTICS: Ordering Points To Identify the Clustering
Structure
 Ankerst, Breunig, Kriegel, and Sander (SIGMOD’99)
 Produces a special order of the database wrt its
density-based clustering structure
 This cluster-ordering contains info equiv to the density-
based clusterings corresponding to a broad range of
parameter settings
 Good for both automatic and interactive cluster
analysis, including finding intrinsic clustering structure
 Can be represented graphically or using visualization
techniques
47

OPTICS: Some Extension from DBSCAN
 Index-based:

k = number of dimensions

N = 20

p = 75%

M = N(1-p) = 5
 Complexity: O(NlogN)
 Core Distance:
 min eps s.t. point is core
 Reachability Distance
D
p2
MinPts = 5
 = 3 cm
Max (core-distance (o), d (o, p))
r(p1, o) = 2.8cm. r(p2,o) = 4cm
o
o
p1
48



Reachability
-distance
Cluster-order
of the objects
undefined
 ‘
49

50
Density-Based Clustering: OPTICS & Its Applications

DENCLUE: Using Statistical Density Functions
 DENsity-based CLUstEring by Hinneburg & Keim (KDD’98)
 Using statistical density functions:
 Major features
 Solid mathematical foundation
 Good for data sets with large amounts of noise

Allows a compact mathematical description of arbitrarily shaped
clusters in high-dimensional data sets
 Significant faster than existing algorithm (e.g., DBSCAN)
 But needs a large number of parameters
f x y e
Gaussian
d x y
( , )
( , )


2
2
2  


N
i
x
x
d
D
Gaussian
i
e
x
f 1
2
)
,
(
2
2
)
( 
 





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i
x
x
d
i
i
D
Gaussian
i
e
x
x
x
x
f 1
2
)
,
(
2
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)
(
)
,
( 
influence of y
on x
total
influence on
x
gradient of x
in the
direction of xi
51

 Uses grid cells but only keeps information about grid cells that do
actually contain data points and manages these cells in a tree-based
access structure
 Influence function: describes the impact of a data point within its
neighborhood
 Overall density of the data space can be calculated as the sum of the
influence function of all data points
 Clusters can be determined mathematically by identifying density
attractors
 Density attractors are local maximal of the overall density function
 Center defined clusters: assign to each density attractor the points
density attracted to it
 Arbitrary shaped cluster: merge density attractors that are connected
through paths of high density (> threshold)
Denclue: Technical Essence
52

Center-Defined and Arbitrary
54

55
Methods
 Summary
55

Grid-Based Clustering Method
 Using multi-resolution grid data structure
 Several interesting methods
 STING (a STatistical INformation Grid approach) by
Wang, Yang and Muntz (1997)
 WaveCluster by Sheikholeslami, Chatterjee, and
Zhang (VLDB’98)

A multi-resolution clustering approach using
wavelet method
 CLIQUE: Agrawal, et al. (SIGMOD’98)

Both grid-based and subspace clustering
56

STING: A Statistical Information Grid Approach
 Wang, Yang and Muntz (VLDB’97)
 The spatial area is divided into rectangular cells
 There are several levels of cells corresponding to different
levels of resolution
57
i-th layer
(i-1)st layer
1st layer

The STING Clustering Method
 Each cell at a high level is partitioned into a number of
smaller cells in the next lower level
 Statistical info of each cell is calculated and stored
beforehand and is used to answer queries
 Parameters of higher level cells can be easily calculated
from parameters of lower level cell
 count, mean, s, min, max

type of distribution—normal, uniform, etc.
 Use a top-down approach to answer spatial data queries
 Start from a pre-selected layer—typically with a small
number of cells
 For each cell in the current level compute the confidence
interval
58

STING Algorithm and Its Analysis
 Remove the irrelevant cells from further consideration
 When finish examining the current layer, proceed to the
next lower level
 Repeat this process until the bottom layer is reached
 Advantages:
 Query-independent, easy to parallelize, incremental
update
 O(K), where K is the number of grid cells at the lowest
level
 Disadvantages:
 All the cluster boundaries are either horizontal or
vertical, and no diagonal boundary is detected
59

60
CLIQUE (Clustering In QUEst)
 Agrawal, Gehrke, Gunopulos, Raghavan (SIGMOD’98)
 Automatically identifying subspaces of a high dimensional data space
that allow better clustering than original space
 CLIQUE can be considered as both density-based and grid-based
 It partitions each dimension into the same number of equal length
interval
 It partitions an m-dimensional data space into non-overlapping
rectangular units
 A unit is dense if the fraction of total data points contained in the unit
exceeds the input model parameter
 A cluster is a maximal set of connected dense units within a
subspace

61
CLIQUE: The Major Steps
 Partition the data space and find the number of points that lie
inside each cell of the partition.
 Identify the subspaces that contain clusters using the Apriori
principle
 Identify clusters
 Determine dense units in all subspaces of interests
 Determine connected dense units in all subspaces of
interests.
 Generate minimal description for the clusters
 Determine maximal regions that cover a cluster of
connected dense units for each cluster
 Determination of minimal cover for each cluster

62
Salary
(10,000)
20 30 40 50 60
age
5
4
3
1
2
6
7
0
20 30 40 50 60
age
5
4
3
1
2
6
7
0
Vacation
(week)
age
Vacation
Salary 30 50
 = 3

63
Strength and Weakness of CLIQUE
 Strength
 automatically finds subspaces of the highest
dimensionality such that high density clusters exist in
those subspaces
 insensitive to the order of records in input and does not
presume some canonical data distribution
 scales linearly with the size of input and has good
scalability as the number of dimensions in the data
increases
 Weakness
 The accuracy of the clustering result may be degraded
at the expense of simplicity of the method

64
Methods
 Summary
64

Assessing Clustering Tendency
 Assess if non-random structure exists in the data by measuring the
probability that the data is generated by a uniform data distribution
 Test spatial randomness by statistic test: Hopkins Static
 Given a dataset D regarded as a sample of a random variable o,
determine how far away o is from being uniformly distributed in the
data space
 Sample n points, p1, …, pn, uniformly from D. For each pi, find its
nearest neighbor in D: xi = min{dist (pi, v)} where v in D
 Sample n points, q1, …, qn, uniformly from D. For each qi, find its
nearest neighbor in D – {qi}: yi = min{dist (qi, v)} where v in D and v ≠
qi
 Calculate the Hopkins Statistic:
 If D is uniformly distributed, ∑ xi and ∑ yi will be close to each other
and H is close to 0.5. If D is highly skewed, H is close to 0
65

Determine the Number of Clusters
 Empirical method
 # of clusters ≈√n/2 for a dataset of n points
 Elbow method
 Use the turning point in the curve of sum of within cluster variance
w.r.t the # of clusters
 Cross validation method
 Divide a given data set into m parts
 Use m – 1 parts to obtain a clustering model
 Use the remaining part to test the quality of the clustering

E.g., For each point in the test set, find the closest centroid, and
use the sum of squared distance between all points in the test
set and the closest centroids to measure how well the model fits
the test set
 For any k > 0, repeat it m times, compare the overall quality measure
w.r.t. different k’s, and find # of clusters that fits the data the best
66

Measuring Clustering Quality
 Two methods: extrinsic vs. intrinsic
 Extrinsic: supervised, i.e., the ground truth is available
 Compare a clustering against the ground truth using
certain clustering quality measure
 Ex. BCubed precision and recall metrics
 Intrinsic: unsupervised, i.e., the ground truth is unavailable
 Evaluate the goodness of a clustering by considering
how well the clusters are separated, and how compact
the clusters are
 Ex. Silhouette coefficient
67

Measuring Clustering Quality: Extrinsic Methods
 Clustering quality measure: Q(C, Cg), for a clustering C
given the ground truth Cg.
 Q is good if it satisfies the following 4 essential criteria

Cluster homogeneity: the purer, the better

Cluster completeness: should assign objects belong to
the same category in the ground truth to the same cluster

Rag bag: putting a heterogeneous object into a pure
cluster should be penalized more than putting it into a rag
bag (i.e., “miscellaneous” or “other” category)

Small cluster preservation: splitting a small category into
pieces is more harmful than splitting a large category into
pieces
68

69
Methods
 Summary
69

Summary
 Cluster analysis groups objects based on their similarity and has wide
applications
 Measure of similarity can be computed for various types of data
 Clustering algorithms can be categorized into partitioning methods,
hierarchical methods, density-based methods, grid-based methods,
and model-based methods
 K-means and K-medoids algorithms are popular partitioning-based
clustering algorithms
 Birch and Chameleon are interesting hierarchical clustering
algorithms, and there are also probabilistic hierarchical clustering
algorithms
 DBSCAN, OPTICS, and DENCLU are interesting density-based
algorithms
 STING and CLIQUE are grid-based methods, where CLIQUE is also a
subspace clustering algorithm
 Quality of clustering results can be evaluated in various ways
70

71
CS512-Spring 2011: An Introduction
 Coverage

Cluster Analysis: Chapter 11
 Outlier Detection: Chapter 12
 Mining Sequence Data: BK2: Chapter 8
 Mining Graphs Data: BK2: Chapter 9

Social and Information Network Analysis

BK2: Chapter 9

Partial coverage: Mark Newman: “Networks: An Introduction”, Oxford U., 2010

Scattered coverage: Easley and Kleinberg, “Networks, Crowds, and Markets:
Reasoning About a Highly Connected World”, Cambridge U., 2010

Recent research papers
 Mining Data Streams: BK2: Chapter 8
 Requirements

One research project
 One class presentation (15 minutes)

Two homeworks (no programming assignment)
 Two midterm exams (no final exam)

References (1)
 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.
 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.
 V. Ganti, J. Gehrke, R. Ramakrishan. CACTUS Clustering Categorical Data
Using Summaries. KDD'99.
72

References (2)
 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. 512-521, 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. 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.
73

References (3)
 G. J. McLachlan and K.E. Bkasford. Mixture Models: Inference and Applications to
Clustering. John Wiley and Sons, 1988.
 R. Ng and J. Han. Efficient and effective clustering method for spatial data mining.
VLDB'94.
 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
 X. Yin, J. Han, and P. S. Yu, “LinkClus: Efficient Clustering via Heterogeneous Semantic
Links”, VLDB'06
74

76
A Typical K-Medoids Algorithm (PAM)
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10
Total Cost = 20
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10
K=2
Arbitrar
y choose
k object
as initial
medoids
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10
Assign
each
remaini
ng
object to
nearest
medoids
Randomly select a
nonmedoid
object,Oramdom
Compute
total cost
of
swapping
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10
Total Cost = 26
Swapping O
and Oramdom
If quality is
improved.
Do loop
Until no
change
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10

77
PAM (Partitioning Around Medoids) (1987)
 PAM (Kaufman and Rousseeuw, 1987), built in Splus
 Use real object to represent the cluster

Select k representative objects arbitrarily
 For each pair of non-selected object h and selected
object i, calculate the total swapping cost TCih
 For each pair of i and h,
 If TCih < 0, i is replaced by h

Then assign each non-selected object to the most
similar representative object

repeat steps 2-3 until there is no change

78
PAM Clustering: Finding the Best Cluster Center
 Case 1: p currently belongs to oj. If oj is replaced by orandom as a
representative object and p is the closest to one of the other
representative object oi, then p is reassigned to oi

79
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)2
) for each iteration
where n is # of data,k is # of clusters
 Sampling-based method
CLARA(Clustering LARge Applications)

80
CLARA (Clustering Large Applications)
(1990)
 CLARA (Kaufmann and Rousseeuw in 1990)

Built in statistical analysis packages, such as SPlus

It draws multiple samples of the data set, applies PAM
on each sample, and gives the best clustering as the
output
 Strength: deals with larger data sets than PAM
 Weakness:

Efficiency depends on the sample size
 A good clustering based on samples will not necessarily
represent a good clustering of the whole data set if the
sample is biased

81
CLARANS (“Randomized” CLARA) (1994)
 CLARANS (A Clustering Algorithm based on Randomized
Search) (Ng and Han’94)
 Draws sample of neighbors dynamically
 The clustering process can be presented as searching a
graph where every node is a potential solution, that is, a
set of k medoids
 If the local optimum is found, it starts with new randomly
selected node in search for a new local optimum
 Advantages: More efficient and scalable than both PAM
and CLARA
 Further improvement: Focusing techniques and spatial
access structures (Ester et al.’95)

82
ROCK: Clustering Categorical Data
 ROCK: RObust Clustering using linKs
 S. Guha, R. Rastogi & K. Shim, ICDE’99
 Major ideas
 Use links to measure similarity/proximity
 Not distance-based
 Algorithm: sampling-based clustering
 Draw random sample
 Cluster with links
 Label data in disk
 Experiments
 Congressional voting, mushroom data

83
Similarity Measure in ROCK
 Traditional measures for categorical data may not work well, e.g.,
Jaccard coefficient
 Example: Two groups (clusters) of transactions
 C1. <a, b, c, d, e>: {a, b, c}, {a, b, d}, {a, b, e}, {a, c, d}, {a, c, e},
{a, d, e}, {b, c, d}, {b, c, e}, {b, d, e}, {c, d, e}
 C2. <a, b, f, g>: {a, b, f}, {a, b, g}, {a, f, g}, {b, f, g}
 Jaccard co-efficient may lead to wrong clustering result
 C1: 0.2 ({a, b, c}, {b, d, e}} to 0.5 ({a, b, c}, {a, b, d})
 C1 & C2: could be as high as 0.5 ({a, b, c}, {a, b, f})
 Jaccard co-efficient-based similarity function:
 Ex. Let T1 = {a, b, c}, T2 = {c, d, e}
Sim T T
T T
T T
( , )
1 2
1 2
1 2



2
.
0
5
1
}
,
,
,
,
{
}
{
)
,
( 2
1 


e
d
c
b
a
c
T
T
Sim

84
Link Measure in ROCK
 Clusters

C1:<a, b, c, d, e>: {a, b, c}, {a, b, d}, {a, b, e}, {a, c, d}, {a, c, e}, {a, d, e}, {b,
c, d}, {b, c, e}, {b, d, e}, {c, d, e}

C2: <a, b, f, g>: {a, b, f}, {a, b, g}, {a, f, g}, {b, f, g}
 Neighbors

Two transactions are neighbors if sim(T1,T2) > threshold
 Let T1 = {a, b, c}, T2 = {c, d, e}, T3 = {a, b, f}

T1 connected to: {a,b,d}, {a,b,e}, {a,c,d}, {a,c,e}, {b,c,d}, {b,c,e}, {a,b,f},
{a,b,g}

T2 connected to: {a,c,d}, {a,c,e}, {a,d,e}, {b,c,e}, {b,d,e}, {b,c,d}

T3 connected to: {a,b,c}, {a,b,d}, {a,b,e}, {a,b,g}, {a,f,g}, {b,f,g}
 Link Similarity

Link similarity between two transactions is the # of common neighbors
 link(T1, T2) = 4, since they have 4 common neighbors

{a, c, d}, {a, c, e}, {b, c, d}, {b, c, e}
 link(T1, T3) = 3, since they have 3 common neighbors

{a, b, d}, {a, b, e}, {a, b, g}

Aggregation-Based Similarity Computation
4 5
10 12 13 14
a b
ST2
ST1
11
0.2
0.9 1.0 0.8 0.9 1.0
For each node nk {
∈ n10, n11, n12} and nl {
∈ n13, n14}, their path-
based similarity simp(nk, nl) = s(nk, n4)·s(n4, n5)·s(n5, nl).
 
 
 
 
171
.
0
2
,
,
3
,
,
14
13 5
5
4
12
10 4





 
 l l
k k
b
a
n
n
s
n
n
s
n
n
s
n
n
sim
After aggregation, we reduce quadratic time computation to
linear time computation.
takes O(3+2) time
86

Computing Similarity with Aggregation
To compute sim(na,nb):
 Find all pairs of sibling nodes ni and nj, so that na linked with ni and nb with
nj.
 Calculate similarity (and weight) between na and nb w.r.t. ni and nj.
 Calculate weighted average similarity between na and nb w.r.t. all such pairs.
sim(na, nb) = avg_sim(na,n4) x s(n4, n5) x avg_sim(nb,n5)
= 0.9 x 0.2 x 0.95 = 0.171
sim(na, nb) can be computed
from aggregated similarities
Average similarity
and total weight 4 5
10 12 13 14
a b
a:
(0.9,3)
b:(0.95,2)
11
0.2
87

88
Methods
 Overview of Clustering Methods
 Summary
88

Link-Based Clustering: Calculate Similarities
Based On Links
Jeh & Widom, KDD’2002: SimRank
Two objects are similar if they are
linked with the same or similar
objects
 The similarity between two
objects x and y is defined as
the average similarity between
objects linked with x and those
with y:
 Issue: Expensive to compute:
 For a dataset of N objects
and M links, it takes O(N2)
space and O(M2
) time to
compute all similarities.
Tom sigmod03
Mike
Cathy
John
sigmod04
sigmod05
vldb03
vldb04
vldb05
sigmod
vldb
Mary
aaai04
aaai05
aaai
Authors Proceedings Conferences
 
   
   
 
 
 
 
 

a
I
i
b
I
j
j
i b
I
a
I
b
I
a
I
C
b
a
1 1
,
sim
,
sim
89

Observation 1: Hierarchical Structures
 Hierarchical structures often exist naturally among objects
(e.g., taxonomy of animals)
All
electronics
grocery apparel
DVD camera
TV
A hierarchical structure of
products in Walmart
Articles
Words
Relationships between articles and
words (Chakrabarti, Papadimitriou,
Modha, Faloutsos, 2004)
90

Observation 2: Distribution of Similarity
 Power law distribution exists in similarities
 56% of similarity entries are in [0.005, 0.015]
 1.4% of similarity entries are larger than 0.1
 Can we design a data structure that stores the significant
similarities and compresses insignificant ones?
0
0.1
0.2
0.3
0.4
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
similarity value
portion
of
entries
Distribution of SimRank similarities
among DBLP authors
91

A Novel Data Structure: SimTree
Each leaf node
represents an object
Each non-leaf node
represents a group
of similar lower-level
nodes
Similarities between
siblings are stored
Consumer
electronics
Apparels
Canon A40
digital camera
Sony V3 digital
camera
Digital
Cameras
TVs
92

Similarity Defined by SimTree
 Path-based node similarity
 simp(n7,n8) = s(n7, n4) x s(n4, n5) x s(n5, n8)
 Similarity between two nodes is the average similarity
between objects linked with them in other SimTrees
 Adjust/ ratio for x =
n1 n2
n4 n5
n6
n3
0.9 1.0
0.9
0.8
0.2
n7 n9
0.3
n8
0.8
0.9
Similarity between two
sibling nodes n1 and n2
Adjustment ratio
for node n7
Average similarity between x and all other nodes
Average similarity between x’s parent and all other
nodes
93

LinkClus: Efficient Clustering via
Heterogeneous Semantic Links
Method
 Initialize a SimTree for objects of each type
 Repeat until stable
 For each SimTree, update the similarities between its
nodes using similarities in other SimTrees

Similarity between two nodes x and y is the average
similarity between objects linked with them
 Adjust the structure of each SimTree

Assign each node to the parent node that it is most
similar to
For details: X. Yin, J. Han, and P. S. Yu, “LinkClus: Efficient
Clustering via Heterogeneous Semantic Links”, VLDB'06
94

Initialization of SimTrees
 Initializing a SimTree
 Repeatedly find groups of tightly related nodes, which
are merged into a higher-level node
 Tightness of a group of nodes
 For a group of nodes {n1, …, nk}, its tightness is
defined as the number of leaf nodes in other SimTrees
that are connected to all of {n1, …, nk}
n1
1
2
3
4
5
n2
The tightness of {n1, n2} is 3
Nodes Leaf nodes in
another
SimTree
95

Finding Tight Groups by Freq. Pattern Mining
 Finding tight groups Frequent pattern mining
 Procedure of initializing a tree
 Start from leaf nodes (level-0)
 At each level l, find non-overlapping groups of similar
nodes with frequent pattern mining
Reduced to
g1
g2
{n1}
{n1, n2}
{n2}
{n1, n2}
{n1, n2}
{n2, n3,
n4}
{n4}
{n3, n4}
{n3, n4}
Transactions
n1
1
2
3
4
5
6
7
8
9
n2
n3
n4
The tightness of a
group of nodes is the
support of a frequent
pattern
96

Adjusting SimTree Structures
 After similarity changes, the tree structure also needs to be
changed
 If a node is more similar to its parent’s sibling, then move
it to be a child of that sibling
 Try to move each node to its parent’s sibling that it is most
similar to, under the constraint that each parent node can
have at most c children
n1 n2
n4 n5
n6
n3
n7 n9
n8
0.8
0.9
n7
97

Complexity
Time Space
Updating similarities O(M(logN)2
) O(M+N)
Adjusting tree structures O(N) O(N)
LinkClus O(M(logN)2
) O(M+N)
SimRank O(M2
) O(N2
)
For two types of objects, N in each, and M linkages between them.
98

Experiment: Email Dataset
 F. Nielsen. Email dataset.
www.imm.dtu.dk/~rem/data/Email-1431.zip
 370 emails on conferences, 272 on jobs,
and 789 spam emails
 Accuracy: measured by manually labeled
data
 Accuracy of clustering: % of pairs of objects
in the same cluster that share common label
Approach Accuracy time (s)
LinkClus 0.8026 1579.6
SimRank 0.7965 39160
ReCom 0.5711 74.6
F-SimRank 0.3688 479.7
CLARANS 0.4768 8.55
 Approaches compared:
 SimRank (Jeh & Widom, KDD 2002): Computing pair-wise similarities
 SimRank with FingerPrints (F-SimRank): Fogaras & R´acz, WWW 2005

pre-computes a large sample of random paths from each object and uses
samples of two objects to estimate SimRank similarity
 ReCom (Wang et al. SIGIR 2003)

Iteratively clustering objects using cluster labels of linked objects
99

WaveCluster: Clustering by Wavelet Analysis (1998)
 Sheikholeslami, Chatterjee, and Zhang (VLDB’98)
 A multi-resolution clustering approach which applies wavelet transform to
the feature space; both grid-based and density-based
 Wavelet transform: A signal processing technique that decomposes a signal
into different frequency sub-band
 Data are transformed to preserve relative distance between objects at
different levels of resolution
 Allows natural clusters to become more distinguishable
100

The WaveCluster Algorithm
 How to apply wavelet transform to find clusters
 Summarizes the data by imposing a multidimensional grid
structure onto data space
 These multidimensional spatial data objects are represented in a
n-dimensional feature space
 Apply wavelet transform on feature space to find the dense
regions in the feature space
 Apply wavelet transform multiple times which result in clusters at
different scales from fine to coarse
 Major features:
 Complexity O(N)
 Detect arbitrary shaped clusters at different scales
 Not sensitive to noise, not sensitive to input order
 Only applicable to low dimensional data
101

102
Quantization
& Transformation
 Quantize data into m-D grid structure,
then wavelet transform
a) scale 1: high resolution
b) scale 2: medium resolution
c) scale 3: low resolution

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