Chapter 9 UNSUPERVISED LEARNING: Clustering Part 1 Cios / Pedrycz / Swiniarski / Kurgan

Chapter 9UNSUPERVISED LEARNING:

Clustering Part 1

Cios / Pedrycz / Swiniarski / KurganCios / Pedrycz / Swiniarski / Kurgan

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Outline

• What is Clustering?- Categories of clustering methods

- Similarity measures • Partition-Based Clustering• Hierarchical Clustering• Model-Based (mixture of probabilities) clustering• Scalable Clustering• Grid-Based Clustering• Cluster Validity• Clustering of Large Datasets

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What is Clustering?

How do we understand data?

We look for structure in data by revealing groups/clusters.

Clusters are about abstraction of data.

The structure is formed based on similarities between patterns (data).

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How hard is clustering?

Consider N data points to be split into “c” groups (clusters). The number of possible splits (partitions) is described as

Even for a small problem of N =100 and c =5 we end up with 1067 partitions

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Clustering Challenges – from Bezdek

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Clustering Challenges – from Bezdek

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Categories of Clustering

We distinguish between three main categories (classes) of clustering methods

• Partition-based

• Hierarchical

• Model-based (mixture of probabilities)

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

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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Partition-Based Clustering

It is also referred to as objective function clustering, relies on the minimization of a certain objective function (performance index)

The result of minimization is a partition matrix and a collection of prototypes

The methods in this class are conceptually and algorithmically appealing

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

21 )( iCp

ki cpE

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Model-Based Clustering

In MBC we assume a certain probabilistic model of data and estimate its parameters, such as mean, covariance matrix, etc.

Mixture density model is the common approach used – we assume that data are a result of a mixture of “c” sources of data and

each source is treated as a separate cluster.

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Similarity measures

SM are the most fundamental components of every clustering method; they are used to quantify similarity (or dissimilarity) between the data points.

The data with the highest similarity (like the lowest distance) are candidates to form a single cluster.

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Examples of Distance Functions:Continuous Data (1)

Euclidean distance (p=2 in Minkowski)

Hamming distance (p=1 in Minkowski)

Tchebyschev distance (p= ∞ in Minkowski)

2ii )y(x),d( yx

1iii |yx|),d( yx

|yx|max),d( iin1,2,...,i yx

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Minkowski distance

(drawing from Wikipedia)

0p ,)y(x),d( pn

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Canberra distance

Example:

v1(1,1,1), v2(1,1,0), v3(10,5,0), v4(1,2,3), v5(2,4,6)

d12=1 d13=2.485 d45=1

Angular separation

Example:

v1(7,6,3,-1), v2(0,3,4,5)

d12=0.363

yx),d( yx

positive are y and x,yx

|yx|),d( ii

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Examples of Distance Functions:Discrete Data (1)

Binary data x = [x1 x2 …xn]

a- number of occurrences where both xi and yi are 1

d- number of occurrences where both xi and yi are 0

b,c- number of occurrences where xi and yi are different (0-1)

1 0 1 a b 0 c d

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Examples of Distances:Discrete Data (2)

Matching index

Rusell & Rao

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Examples of Distances:Discrete Data (3)

Jacard index

Czekanowski

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Hierarchical Clustering

HC provides graphical illustration of relationships between the data in the form of dendrogram, which is a binary tree.

There are two approaches to HC:

• Bottom – up / agglomerative

• Top-down / divisive

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• Agglomerative / bottom-up method starts with each object in the data forming its own cluster, and then successively merges the clusters until one large cluster is formed, which encompasses the entire dataset

• Divisive / top-down method starts by considering the entire data as one cluster and then splits up the cluster(s) until each object forms its own cluster

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a b c d e f g h

{a} {b,c,d,e} {f,g,h}

Top –down /divisive

Bottom-up /agglomerative

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ba dc e

{a}, {b}, {c, d}, {e}

{a,b}, {c, d}, {e}

{a,b,c,d}, {e}

numbers of clusters at different levels4

Hierarchical Agglomerative Clustering

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Given : a data set and the distance function 1. start with “N “ clusters by assigning each pattern to a separate cluster 2. proceed with this initial configuration of the clusters and merge the clusters that are the closest. In other words, if S and T are the two clusters being recognized as the closest, form a single cluster {S, T} and reduce the number of clusters by one 3 repeat step 2 until a minimal number of the clusters has been reached. Result : clusters of data (partition)

Hierarchical Agglomerative Clustering

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yx)()(

1S-T :linkage average

maxST:linkage complete

minST:method linkage Single

TcardScard

Distance Between Clusters

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S imilarity between S and T is calculated based on the minimal distance between the elements belonging to the corresponding clusters

Single Linkage

|| T S||minxTyS

||x y||

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Complete Linkage

|| T S||maxxTyS

||x y||

We rely on the maximal distance between the patterns in the analyzed clusters.

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Average Linkage

|| T S||1

card(T)card(S)xTyS

||x y ||

We combine two clusters based upon their averaged distance between the patterns in the clusters.

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Hausdorff Distance Function

)},d(minmax),,d(minmax{max B) ,d(A ABBA yxyx xyyx

picture from Wikipedia

d(A,B) = max { min d(A,B)} = sup { inf d(A,B)}

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Lance-Williams updating formula

|dd|γβddαdαd CB,CA,BA,CB,BCA,ACB,A

Clustering

method A (B)

Single link 1/2 0 -1/2 Complete link 1/2 0 1/2 centroid

median 1/2 -1/4 0

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Hierarchical Divisive Method

HD algorithm starts by considering all divisions of the data into two nonempty subsets

which amounts to possibilities.

However, it is possible to construct divisive methods that do not consider all divisions,

most of which may be incorrect anyway.

One such algorithm is by MacNaughton - Smith (1964)

12 1 n

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At first A:=CA:=C and B:=B:=1. Move one object at a time from A to B.

For each object iA we compute average dissimilarity to all other objects of A:

Object m of A for which a(m) is the largest, is moved to B:

ia ),(1||

}{:},{\: mBmAA

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2. Move other objects from A to B (called the “splinter group”)

If |A|=1, stop. Otherwise compute a(i) for all iA, and the average dissimilarity of i to all objects of B, denoted as d(i,B)

Bidia ),(||

1),()(

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Select the object hA for which

If a(h)-d(h,B) > 0 move h from A to B, go to 2

If a(h)-d(h,B) 0 the process stops

The division of C into clusters A and B is complete.

)),()((max),()( BidiaBhdha Ai

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Object Average Dissimilarity to the Other Objects

a (2.0 + 6.0 + 10.0 + 9.0)/4 = 6.75

b (2.0 + 5.0 + 9.0 +8.0)/4 = 6.00

c (6.0 + 5.0 + 4.0 + 5.0)/4 = 5.00

d (10.0 + 9.0 + 4.0 + 3.0)/4 = 6.50

e (9.0 + 8.0 + 5.0 + 3.0)/4 = 5.25

0.00.30.50.80.9

0.30.00.40.90.10

0.50.40.00.50.6

0.80.90.50.00.2

0.90.100.60.20.0

In our example, object a is chosen to initiate the splinter group.

At this stage we have groups A={b,c,d,e} and B={a}, but we don’t stop here.

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Average DissimilarityAverage DissimilarityAverage DissimilarityAverage Dissimilarity to Objects of to Objects of

ObjectObject to remaining Objects to remaining Objects Splinter Group Splinter Group DifferenceDifference

b (5.0+9.0+8.0)/37.33 2.00 5.33 c (5.0+4.0+5.0)/34.67 6.00 -1.33 d (9.0+4.0+3.0)/35.33 10.00 -4.67 e (8.0+5.0+3.0)/35.33 9.00 -3.67

Therefore, object b changes sides, so new splinter group is B={a, b} and the remaining group becomes A={c, d, e}

Average DissimilarityAverage DissimilarityAverage DissimilarityAverage Dissimilarity to Objects of to Objects of

ObjectObject to remaining Objects to remaining Objects Splinter Group Splinter Group DifferenceDifference c (4.0+5.0)/2=4.50 (6.0+5.0)/2=5.50 -1.00 d (4.0+3.0)/2=3.50 (10.0+9.0)/2=9.50 -6.00 e (5.0+3.0)/2=4.00 (9.0+8.0)/2=8.50 -4.50

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Objective Function Clustering

Develop and optimize a partition matrix so that a certain performance index is optimized.

The lower the value of the objective function, the better.

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Objective function Minimization Structure

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• Depends on minimization of a certain performance index Q

c – number of clustersU – partition matrix

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Clustering: representation issues

clusters?represent tohow

0 1 1 0 0 0 0 0

0 0 0 0 0 1 1 0

1 0 0 1 1 0 0 1

clusters

points data

matrix Partition

matrix partitiondata

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Partition Matrix

0 1 1 0 0 0 0 0

0 0 0 0 0 1 1 0

1 0 0 1 1 0 0 1

U{6,7}:cluster3

{2,3}:cluster2

{1,4,5,8} :1cluster

U {U | 0 u ik N, k 1

0 u ik 1 } i1

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Objective Function-Based Clustering

Clustering is guided by the minimization of some objective function/performance index Q.

Representation of structure is in the form of a:• Partition matrix U = [uik], i=1,2,…,c; k=1, 2,…,N

•Prototypes vi, i=1,2,…, c

1iN..., 2, 1,kfor 1

1kc ..., 2, 1,ifor N

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Algorithm:

Given: the (guessed!) number of clusters (c), decide on the similarity function

(and on the value of the power factor (m) - for fuzzy clustering only)

Compute the prototypes (v) and update the partition matrix (U) based on the conditions of the minimized objective function

Result: partition matrix and prototypes

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K - means Algorithm

Inputc = number of clusters

d = distance function

m = power (fuzziness) factor not used in hard K- means

t = termination criteriaAmount of movement between clusters

v = cluster centersRandomly chosen each run

Output

U = partition matrix

V = Cluster centers

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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 partition (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 no more new

assignment

The K-Means Clustering Method

• Example

0 1 2 3 4 5 6 7 8 9 100

0 1 2 3 4 5 6 7 8 9 10

Arbitrarily choose K object as initial cluster center

Assign each objects to most similar center

Update the cluster means

reassignreassign

Comments on the K-Means Method

• Strength: Relatively 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 optimum. The global optimum

may be found using techniques such as: deterministic annealing and

genetic algorithms

• Weakness

– Applicable only when mean is defined, then what about categorical

– Need to specify k, the number of clusters, in advance

– Unable to handle noisy data and outliers

– Not suitable to discover clusters with non-convex shapes

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

0 1 2 3 4 5 6 7 8 9 100

0 1 2 3 4 5 6 7 8 9 10

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Termination criteria

When the summed difference of the old and new partitions (partition matrices) is less than a threshold

• Hard Unew - Uold == 0;

• FuzzyUnew - Uold < user chosen number

(like 0.0001)

K - means Algorithm

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Minimizes the objective function by allocating data points to different clusters

Given: the number of clusters c

1. select initial c means

2. calculate distance between the pattern and the means of the clusters

3. allocate the pattern to the cluster whose mean is nearest to this pattern

4. recalculate the mean of the cluster from the patterns allocated to it

5. repeat 2-4 until a termination criterion is satisfied

Result: a collection of means (prototypes) of the clusters

K - means Algorithm

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K-Means Clustering Algorithm (1)

Objective function 2N

1kikik

1i||||uQ

Minimize Q

subject to constraintsuik = 0 or 1

1iN..., 2, 1,kfor 1

1kc ..., 2, 1,ifor N

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K-Means Clustering Algorithm (2)start with some initial configuration of the prototypes vi, i=1,2, …, c (e.g., choose

them randomly) - iterate - construct a partition matrix by assigning numeric values to U according to the

following rule

otherwise 0,

),d(min),d( if 1,u jkijik

- update the prototypes by computing the weighted average that involves the entries of the partition matrix

until the performance index Q stabilizes and does not change or the changes are negligible

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Growing the Hierarchy of Clusters

c-clusters

c-clusters (a)

Develop “c” clusters; split the most heterogeneous clusterinto “c” clusters, etc.

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Growing the Hierarchy of Clusters

c-clusters

c-clusters balanced growth

imbalanced growth

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Kernel-Based Clustering

Idea:Original data in the n-dimensional space are transformedthrough some mapping into elements in m-dimensional space where m >>n.

Objective function in the new space:

1i||)φ()φ(||uQ vx

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Kernel-Based Clustering

Given the dimensionality of a new space, m >>n, we calculate in the new space a kernel function K(x,v) as a dot product

K(x,v) = T(x)(v)

We can use a Gaussian kernel

K(x,v) = exp(- ||x-v||2/2)

||(xk)-(vi)||2 = 2 – K(xk, vi)

Kernel K-means

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k-means cannot separate clusters that are non-linearly separable

To solve this problem kernel k-means algorithm is used:

before doing clustering, all points are mapped into a higher-dimensional space using some nonlinear function, and then the algorithm partitions the points in the new space.

Major difference with k-means is calculation of distance in the kernel k-means algorithm by the kernel method - not, say, by Euclidean.

Kernel K-means

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||m)φ(||w(x)Q

1i||)φ()φ(||uQ vx

To calculate the distances between the points in the new space and the mj we use a kernel function that is specified in the kernel matrix K.

Kernel K-meansInput: K - kernel function, k - number of clusters

1. Initialize the k clusters: C1(0), ...,Ck(0)

2. Set t = 0

3. For each point x, find its new cluster by:

J*(x) = argmin j ||(x)−mj||2

4. Compute the updated clusters as

Cj (t+1) = {x : J*(x) = J}

5. If not converged, set t = t + 1 and go to step 3; otherwise, stop

Result: partition into clusters C1, ....,Ck

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K-Medoids Clustering

To enhance robustness of clustering we use medoids instead of prototype mean values

In one-dimensional case it is the median

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-sampling61

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Median as a Robust Estimator

Consider an ordered collection of real data

x1 <= x2 <= … <=xN

Median is the central point in this sequence (if N is even) or an average the two points in the middle (if N is odd)

Median is a robust estimator (ordered statistics)

median

outlier

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Median as a Robust Estimator

Median is a solution to the minimization problem

|medx||xx|minN

We enhance the robustness by considering the objective function

|vx|uN

1kijjkik

Advantage: one of the original points becomes cluster center

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Partitioning Around Medoids (PAM)

medoids – a family of the most centrally positioned data points. PAM clustering:

represent the structure in the data by a collection of medoids, each data point is grouped around the medoid to which its distance is the

shortest. PAM starts with an arbitrary collection of elements treated as medoids. At each step of the optimization, we make a swap between a certain data and one of the medoids assuming that the swap results in improvement of the quality of the clustering. Limitations -- size of the dataset. PAM works well for small datasets with a small number of clusters, (100 data points and 5 clusters).

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

PAM Clustering: Finding the Best Cluster Center

Four Cases

• Case 1Suppose p currently belongs to cluster represented by Oj. Further

More, D(p,Oi) < D(p,Orandom) .Then If Oj is replaced by Orandom, p will belong to

the cluster represented by Oi

swap cost: C=d(p,Oi)-d(p,Oj)

• Case 2P currently belongs to the cluster represented by Oj. But this time we

assume D(p,Oi) > D(p,Orandom). So, Then If Oj is replaced by Orandom, p will belong to the cluster represented by Orandom

Cost Swap C=d(p,Orandom)-d(p,Oj)

• Case 3P currently belongs to a cluster represented by Oi instead of Oj,

Besides, D(p,Oi) < D(p,Orandom) . Then If Oj is replaced by Orandom, p will still belong to the cluster represented by Oi

Cost Swap C=0;

• Case 4P currently belongs to cluster represented by Oi, But D(p,Oi) >

D(p,Orandom)If replacing Oj with Orandom, p will belong to Orandom

Cost Swap C=d(p,Orandom)-d(p,Oi)

PAM: A Typical K-Medoids Algorithm

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

Arbitrary choose k object as initial medoids

0 1 2 3 4 5 6 7 8 9 10

Assign each remaining 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

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

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)

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

CLARA• Algorithm CLARA

Five Examples of size 40+2k

1. For i =1 to 5, repeat the following steps:

2. Draw a sample of 40 + 2k objects randomly from the entire data set, and call Algorithm PAM to find k medoids of the sample.

3. For each object Oj in the entire data set, determine which of the k medoids is the most similar to Oj.

4. Calculate the average dissimilarity of the clustering obtained in the previous step. If this value is less than the current minimum, use this value as the current minimum, and retain the k medoids found in Step 2 as the best set of medoids obtained so far.

5. Return to Step 1 to start the next iteration

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)

CLARANS

• Find k medoids can be viewed abstractly as searching through a certain graph.

• node is represented by a set of k objects {O1,……Ok} which is selected medoids.

• Two nodes are neighbors if and only if their sets differ by only one object. Each node corresponds to a clustering and each node can be assigned a cost which defines the dissimilarity between every object and the medoit

CLARANS• Algorithm CLARANS 1. Input parameters numlocal and maxneighbor. Initialize i to 1, and

mincost to a large number. 2. Set current to an arbitrary node in Gn,k. 3. Set j to 1. 4. Consider a random neighbor S of current, and based on cost swap

functtion, calculate the cost differential of the two nodes. 5. If S has a lower cost, set current to S, and go to Step 3. 6. Otherwise, increment j by 1. If j < maxneighbor, go to Step 4. 7. Otherwise, when j > maxneighbor, compare the cost of current with

mincost. If the former is less than mincost, set mincost to the cost of current and set bestnode to current.

8. Increment i by 1. If i > numlocal, output bestnode and halt. Otherwise, go to Step 2.

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Clustering Large Applications (CLARA)

Modification of PAM to deal with large data sets:

Instead of processing all data, sample it ,and apply PAM to the sample

K-Medoids clustering:

• PAM

• CLARA

Advantages: • robustness, and • interpretability

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Fuzzy C-Means

How to deal (quantify) data that are in-between clusters?Consider partial membership to clusters – emergence of fuzzy sets

elements with partial membership

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Fuzzy C-Means

Partial membership in clusters – fuzzy partition matrix U

Objective function

U = [uik]; uik – degree of membership of k-th data to i-th cluster

m – fuzzification coefficient, m>1

||. || - distance function

1i||||uQ

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Fuzzy C - Means: Optimization

QMin||||uQ U,prototypes2

n..., 1,2,j ,...,2,1,0v

prototypes respect to with Q

cithat

prototypes

!matrix! partitiona is U:constraint

N..., 1,2,k c,...,2,1i,0u

Q is that

matrix partition respect to withQ Min

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1iprototypes ||||umin vx

(xk vi )T (xk vi ) 2 uikm

(xk vi )

(xk vi ) 0

Fuzzy C – Means: Calculations

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FCM: AlgorithmInitialize: select the number of clusters (c), stopping value (e), fuzzification coefficient (m). The distance function is Euclidean or weighted Euclidean. The initial partition matrix consists of random entries

Repeat update prototypes

update partition matrix

1)2/(m

||||||||

until a certain stopping criterion has been satisfied

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FCM – Algorithm

Design aspects

stopping criterion: termination of iterations

fuzzification coefficient (m) : m>1 Shape of the membership functions m =2.0 – typical value m close to 1 – set like shape of membership functions m higher than 2.0 - spike like membership functions

maxik | uik(iter+1) – uik(iter)|

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Model-Based Clustering

Mixture of data as an underlying model

Each component described by some conditional probabilitydensity function described by parameters

p(x|θ 1, θ 2,…, θ c) =

1iii )p|p( θx

Parameters estimation of the mixture of data

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Mixture of Data Model

Maximum likelihood estimation

Given data x1, x2, …, xN choose parameters such that the value of the expression

becomes maximized

)|p()|P(N

1kk θxθX

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Scalable Clustering

Clustering algorithms need to be scalable to deal with large data sets

Example algorithms:

•Density-Based Clustering (DBSCAN)

•OPTICS

•DENCLUE

•CLIQUE

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) 88

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

Kurgan90

Density-Based Clustering DBSCAN

-neighborhood, denoted by N(xk), is given as

N(xk) = { x | d(x, xk) }

Based on the concept of -neighborhood, N_Pts, and density-based reachability

N_Pts – number of points falling within the neighborhood

xi is xk density-reachable with parameters and N_Pts ifthe following conditions are satisfied

(a) xi belongs to N(xk), and (b) card (N(xk)) N_Pts

Then xi becomes a CORE point

Kurgan91

DBSCAN: Density-based Reachability

xi is density-reachable from xk by a chain of data points xk+1, xk+2, …, xi-1

N(x1) N(xk)

Kurgan92

DBSCAN Algorithm

Set up the parameters of the neighborhood, and N_Pts (a) arbitrarily select a data point, say xk

(b) find (retrieve) all data that are density reachable from xk

(c) if xk is a core point, then the cluster has been formed (all points density reachable from xk)

(d) otherwise consider xk to be a border point and move on to the next data

point The sequence (a) – (d) is repeated until all data points have been processed.

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

DBSCAN: Density-Based Spatial Clustering of Applications with Noise

• Relies on a density-based notion of cluster: A cluster is defined as a maximal set of density-connected points

• Discovers clusters of arbitrary shape in spatial databases with noise

Border

Outlier

Eps = 1cm

MinPts = 5

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

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

OPTICS: Some Extension from DBSCAN

• Index-based: • k = number of dimensions • N = 20• p = 75%• M = N(1-p) = 5

– Complexity: O(kN2)• Core Distance

• Reachability Distance

MinPts = 5

= 3 cm

Max (core-distance (o), d (o, p))

r(p1, o) = 2.8cm. r(p2,o) = 4cm

DENCLUE: Using Statistical Density Functions

f x y eGaussian

( , )( , )

DENCLUE: Using Statistical Density Functions

• Density AttractorThe local maxima of the overall density function

• Density AttractedA point x is density attracted to a density attractor x* if there exists a set

of points x0,x1…,xk such that x0=x, xk=x* and the gradient of xi-1 is in the direction of xi for 0<i<k

In General Points that are density attracted to x* may form a cluster

• center-defined clusterA center-defined cluster for a density attractor, x* is a subset of points, C

belongs to D ,that are density-attracted by x*, and where the density function x* is no less than a threshold.

• arbitrary-shape clusterFor a set of density attractor is a set of Cs, each being density-attracted to

its respective density attractor, where

(1)Density function value at each density-attractor is no less than a threshold

(2) there exist a path P, from each density-attractor to another, where the density function value for each point along the path is no less than the threshold

Denclue

Kurgan101

Grid-Based Clustering

Describe structure in data in the language of genericgeometric constructs – hyperboxes and their combinations

Collection of clusters of

different geometry

Formation of clusters by merging adjacent hyperboxes

of the grid

Kurgan102

Grid-Based Clustering

Hyperboxes

{ B1, B2, …, Bp.} with two requirements: a) Bi is nonempty in the sense it includes some data points, b) the hyperboxes are disjoint that is Bi Bj = if i j, c) a union of all hyperboxes covers all data that is X

where X = {x1, x2, …, xN}. It is also required that such hyperboxes “cover” some maximal number

(say bmax) of data points.

Kurgan103

Grid-Based Clustering Steps Formation of the grid structure Insertion of data into the grid structure Computation of the density index of each hyperbox of the grid

structure Sorting the hyperboxes with respect to the values of their density

index Identification of cluster centres (viz. the hyperboxes of the highest

density) Traversal of neighboring hyperboxes and merging process Choice of the grid: too rough grid may not help capture the details of the structure in the

data. too detailed grid produces a significant computational overhead.

Clustering High-Dimensional Data

• Clustering high-dimensional data

– Many applications: text documents, DNA micro-array data

– Major challenges:

• Many irrelevant dimensions may mask clusters

• Distance measure becomes meaningless—due to equi-distance

• Clusters may exist only in some subspaces

• Methods

– Feature transformation: only effective if most dimensions are relevant

• PCA & SVD useful only when features are highly correlated/redundant

– Feature selection: wrapper or filter approaches

• useful to find a subspace where the data have nice clusters

– Subspace-clustering: find clusters in all the possible subspaces

• CLIQUE, ProClus, and frequent pattern-based clustering

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

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

20 30 40 50 60age

Salary 30 50

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

Reference

• Data Mining Concepts and Techniques, edited by Jiawei Han

• Jiawei Han, CLARANS: A Method for Clustering Objects for Spatial Data Mining

Chapter 9 UNSUPERVISED LEARNING: Clustering Part 1 Cios / Pedrycz / Swiniarski / Kurgan

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