Data Mining: Concepts and Techniques

(3rd ed.)

Chapter 10. Cluster Analysis

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Outline

CLUSTER ANALYSIS: BASIC CONCEPTS PARTITIONING ALGORITHMS

K-MEANS K-MEDOIDS

HIERARCHICAL CLUSTERING DENSITY-BASED CLUSTERING EVALUATION OF CLUSTERING SUMMARY

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CLUSTER ANALYSIS: BASIC CONCEPTS

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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

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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

Applications

Clustering the website visitors based on age, income, and recency

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Applications

Clustering customers based on their profile

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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

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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

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How to

Grouping the following customers based on their profiles

How to: define distance function? Assign weights to attributes?

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No age income student credit_rating buys_computer1 <=30 high no fair no2 <=30 high no excellent no3 31…40 high no fair yes4 >40 medium no fair yes5 >40 low yes fair yes6 >40 low yes excellent no7 31…40 low yes excellent yes8 <=30 medium no fair no9 <=30 low yes fair yes

10 >40 medium yes fair yes11 <=30 medium yes excellent yes12 31…40 medium no excellent yes13 31…40 high yes fair yes14 >40 medium no excellent no

Measure the Quality of Clustering

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

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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)

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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

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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

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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

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PARTITIONING ALGORITHMS

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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

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K-MEANS

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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 objectsLoop if needed

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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

K-Means Algorithm

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Demo

K-means Algorithm Demo - YouTube https://www.youtube.com/watch?v=zHbxbb2ye

3E

K-means - Interactive demo http://home.deib.polimi.it/matteucc/Clustering/t

utorial_html/AppletKM.html

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Example

Data: 4 types of medicines and each has two attributes (pH and weight index).

Goal: to group these objects into K=2 group of medicine.

Medicine Weight pH-Index

A 1 1

B 2 1

C 4 3

D 5 4

A B

C

D

Example Step 1: Use initial seed points for partitioning

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Assign each object to the cluster with the nearest seed point

Euclidean distance

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Example Step 2: Compute new centroids of the current

partition

Knowing the members of each cluster, now we compute the

new centroid of each group based

on these new memberships.

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Example Step 2: Renew membership based on new

centroids

Compute the distance of all objects to the new

centroids

Assign the membership to objects

Example Step 3: Repeat the first two steps until its

convergence

Knowing the members of each cluster, now we compute the

new centroid of each group based

on these new memberships.

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Example Step 3: Repeat the first two steps until its

convergence

Compute the distance of all objects to the new centroids

Stop due to no new assignment Membership in each cluster no longer change

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ExerciseFor the medicine data set, use K-means with the Manhattan distance

metric for clustering analysis by setting K=2 and initialising seeds as

C1 = A and C2 = C. Answer three questions as follows:

1. How many steps are required for convergence?

2. What are memberships of two clusters after convergence?

3. What are centroids of two clusters after convergence?

Medicine Weight pH-Index

A 1 1

B 2 1

C 4 3

D 5 4

A B

C

D

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 31

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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K-MEDOIDS

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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

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Total Cost = 20

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Arbitrary choose k object as initial medoids

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Assign each remaining object to nearest medoids Randomly select a

nonmedoid object,Oramdom

Compute total cost of swapping

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Swapping O and Oramdom

If quality is improved.

Do loop

Until no change

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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

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K-Medoid Algorithm

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HIERARCHICAL CLUSTERING

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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

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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)

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Agglomerative versus Divisive

A hierarchical clustering method can be either agglomerative or divisive, depending on whether the hierarchical decomposition is formed in a bottom-up (merging) or top-down (splitting) fashion.

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Distance between clusters

Proximity matrix: contains the distance between each cluster

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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

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Single-Linkage Clustering

The algorithm is an agglomerative scheme that erases rows and columns in the proximity matrix as old clusters are merged into new ones.

The N*N proximity matrix is D = [d(i,j)]. The clusterings are assigned sequence numbers 0,1,......, (n-1) and L(k) is the level of the kth clustering. A cluster with sequence number m is denoted (m) and the proximity between clusters (r) and (s) is denoted d [(r),(s)].

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AGNES algorithm

1. Begin with the disjoint clustering having level L(0) = 0 and sequence number m = 0.

2. Find the least dissimilar pair of clusters in the current clustering, say pair (r), (s), according to

d[(r),(s)] = min d[(i),(j)]where the minimum is over all pairs of clusters in the current clustering.

3. Increment the sequence number : m = m +1. Merge clusters (r) and (s) into a single cluster to form the next clustering m. Set the level of this clustering to

L(m) = d[(r),(s)]

4. Update the proximity matrix, D, by deleting the rows and columns corresponding to clusters (r) and (s) and adding a row and column corresponding to the newly formed cluster. The proximity between the new cluster, denoted (r,s) and old cluster (k) is defined in this way:

d[(k), (r,s)] = min d[(k),(r)], d[(k),(s)]

5. If all objects are in one cluster, stop. Else, go to step 2.

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Example

Hierarchical clustering of distances in kilometers between the following Italian cities.

TO-Torino MI-Milan FI-Firenze RM-Rome NA-Napoli BA-Bari

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Example (single-linkage)

  BA FI MI NA RM TO

BA 0 662 877 255 412 996

FI 662 0 295 468 268 400

MI 877 295 0 754 564 138

NA 255 468 754 0 219 869

RM 412 268 564 219 0 669

TO 996 400 138 869 669 0

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Input distance matrix (L = 0 for all the clusters)

The nearest pair of cities is MI and TO, at distance 138. These are merged into a single cluster called "MI/TO". The level of the new cluster is L(MI/TO) = 138 the new sequence number is m = 1.

Example (single-linkage)

  BA FI MI/TO NA RM

BA 0 662 877 255 412

FI 662 0 295 468 268

MI/TO 877 295 0 754 564

NA 255 468 754 0 219

RM 412 268 564 219 0

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Compute the distance from this new compound object to all other objects. In single link clustering the rule is that the distance from the compound object to another object is equal to the shortest distance from any member of the cluster to the outside object.

After merging MI with TO we obtain the following matrix:

min d(i,j) = d(NA,RM) = 219 => merge NA and RM into a new cluster called NA/RML(NA/RM) = 219m = 2

Example (single-linkage)

  BA FI MI/TO NA/RM

BA 0 662 877 255

FI 662 0 295 268

MI/TO 877 295 0 564

NA/RM 255 268 564 0

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min d(i,j) = d(BA,NA/RM) = 255 => merge BA and NA/RM into a new cluster called BA/NA/RML(BA/NA/RM) = 255m = 3

Example (single-linkage)

  BA/NA/RM FI MI/TO

BA/NA/RM 0 268 564

FI 268 0 295

MI/TO 564 295 0

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min d(i,j) = d(BA/NA/RM,FI) = 268 => merge BA/NA/RM and FI into a new cluster called BA/FI/NA/RML(BA/FI/NA/RM) = 268m = 4

Example (single-linkage)

  BA/FI/NA/RM MI/TO

BA/FI/NA/RM 0 295

MI/TO 295 0

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Finally, we merge the last two clusters at level 295.

Example (single-linkage)

The process is summarized by the following hierarchical tree:

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138219

255

268

295

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

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Dendrogram: Shows How Clusters are Merged

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K=3

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

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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(K i, 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(K i,

Kj) = dist(Ci, Cj)

Medoid: distance between the medoids of two clusters, i.e., dist(K i,

Kj) = dist(Mi, Mj)

Medoid: a chosen, centrally located object in the cluster

X X

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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

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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 modeling57

DENSITY-BASED CLUSTERING

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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

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Convex regions inaccurately identified, noise or outliers are included in the clusters.

Density-Based Clustering Methods

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)

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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

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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

qp1

p q

o

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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

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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

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DBSCAN: Sensitive to Parameters

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EVALUATION OF CLUSTERING

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how to evaluate whether the clustering results are good?

Assessing clustering tendency. In this task, for a given data set, we assess whether a nonrandom structure exists in the data.

Determining the number of clusters in a data set. Measuring clustering quality.

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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 data set D regarded as a sample of a random variable o,

determine how far away o is from being uniformly distributed in the data space.n Sample n points, p1, . . . , pn, uniformly from D. That is, each

point in D has the same probability of being included in this sample. For each point, pi, we find the nearest neighbor of pi in D, and let xi be the distance between pi and its nearest neighbor in D. That is,

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Assessing Clustering Tendency

2. Sample n points, q1, . . . , qn, uniformly from D. For each qi . we find the nearest neighbor of qi in D-{qi}, and let yi be the distance between qi and its nearest neighbor in D-{qi}. That is,

3. Calculate the Hopkins Statistic:

4. If D is uniformly distributed, ∑ xi and ∑ yi will be close to each other and H is close to 0.5.

5. If D is highly skewed, H is close to 0

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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

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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

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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

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SUMMARY

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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

AGNES and DIANA are hierarchical clustering algorithms, and there are also probabilistic hierarchical clustering algorithms

DBSCAN, OPTICS, and DENCLU are interesting density-based algorithms

Quality of clustering results can be evaluated in various ways

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