# Cluster Algorithms. Adriano Cruz 28 de outubro de 2013

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1 Cluster Algorithms Adriano Cruz 28 de outubro de 2013 Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

2 Summary 1 K-Means Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

3 Summary 1 K-Means 2 Fuzzy C-means Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

4 Summary 1 K-Means 2 Fuzzy C-means 3 Possibilistic Clustering Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

5 Summary 1 K-Means 2 Fuzzy C-means 3 Possibilistic Clustering 4 Gustafson-Kessel Algorithm Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

6 Summary 1 K-Means 2 Fuzzy C-means 3 Possibilistic Clustering 4 Gustafson-Kessel Algorithm 5 Gath-Geva Algorithm Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

7 Summary 1 K-Means 2 Fuzzy C-means 3 Possibilistic Clustering 4 Gustafson-Kessel Algorithm 5 Gath-Geva Algorithm 6 K-medoids Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

8 Section Summary 1 K-Means 2 Fuzzy C-means 3 Possibilistic Clustering 4 Gustafson-Kessel Algorithm 5 Gath-Geva Algorithm 6 K-medoids Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

9 K-Means Algorithm 1 Based on the Euclidean distances among elements of the cluster. 2 Centre of the cluster is the mean value of the objects in the cluster. 3 Classifies objects in a hard way. 4 Each object belongs to a single cluster. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

10 Initial Definitions 1 Consider n objects and c clusters. 2 Each object x e X is defined by l characteristics x e = (x e,1,x e,2,...,x e,l ). 3 Consider A a set of c clusters (A = A 1,A 2,...,A c ). Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

11 K-Means Properties 1 The union of all c clusters makes the Universe i c A i = X. 2 No element belongs to more than one cluster. i,j c : i j A i A j = 3 There is no empty cluster A i X. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

12 Membership Function { 1 xe A χ Ai (x e ) = i 0 x e / A i c c χ Ai (x e ) χ ie = 1, e i=1 i=1 χ ie χ je = 0, e n 0 < χ ie < n e=1 Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

13 Membership Matrix U 1 Matrix containing the values of inclusion of each element into each cluster (0 or 1). 2 Matrix has c (clusters) lines and n (elements) columns. 3 The sum of all elements in the column must be equal to one (element belongs only to one cluster 4 The sum of each line must be less than n e grater than 0. No empty cluster, or cluster containing all elements. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

14 Matrix Examples 1 Two examples of clustering. 2 What do the matrices represent? Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

15 Matrix Examples contd 1 Matrices U 1 and U 2 represent the same clusters. U U Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

16 K-Means inputs and outputs 1 Inputs: the number of clusters c and a database containing n objects with l characteristics each. 2 Output: A set of c clusters that minimises the square-error criterion. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

17 K-Means Algorithm v1 Arbitrarily assigns each object to a cluster (matrix U). repeat Update the cluster centres; Reassign objects to the clusters to which the objects are most similar; until no change; Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

18 K-Means Algorithm v2 Arbitrarily choose c objects as the initial cluster centres. repeat Reassign objects to the clusters to which the objects are most similar; Update the cluster centres; until no change; Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

19 Algorithm details 1 The algorithm tries to minimize the function J(U,v) = n e=1 i=1 c χ ie (d ie ) 2 2 (d ie ) 2 is the distance between the element x e (m characteristics) and the centre of the cluster i (v i ) d ie = d(x e v i ) d ie = x e v i [ l d ie = (x ej v ij ) 2] 1/2 j=1 Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

20 Cluster Centres 1 The centre of the cluster i (v i ) is an l characteristics vector. 2 The jth co-ordinate is calculated as v ij = n χ ie x ej e=1 n e=1 χ ie Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

21 Detailed Algorithm Choose c (number of clusters). Set error (ε > 0) and step (r = 0). Arbitrarily set matrix U(r). Do not forget, each element belongs to a single cluster, no empty cluster and no cluster has all elements. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

22 Detailed Algorithm cont. repeat Calculate the centre of the clusters vi r Calculate the distance d r i of each point to the centre of the clusters Generate U r+1 recalculating all characteristic functions using the equation { 1 d χ r+1 r ie = ie = min(d r je ), j k 0 until U r+1 U r < ε Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

23 Matrix Norms 1 Consider a matrix U of n lines and n columns: n 2 Column norm = A 1 = max a ij 1 j n 3 Line norm = A = max 1 i n j=1 i=1 n a ij Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

24 Stop criteria 1 Some implementatios use the value of the objective function to stop the algorithm 2 The algorithm will stop when the objective function improvement between two consecutive iterations is less than the minimum amount of improvement specified. 3 The objective function is J(U,v) = n e=1 i=1 c χ ie (d ie ) 2 Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

25 K-Means Problems 1 Suitable when clusters are compact clouds well separated. 2 Scalable because computational complexity is O(nkr). 3 Necessity of choosing c is disadvantage. 4 Not suitable for non convex shapes. 5 It is sensitive to noise and outliers because they influence the means. 6 Depends on the initial allocation. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

26 Examples of Result Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

27 Original x Result Data Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

28 Section Summary 1 K-Means 2 Fuzzy C-means 3 Possibilistic Clustering 4 Gustafson-Kessel Algorithm 5 Gath-Geva Algorithm 6 K-medoids Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

29 Fuzzy C-means 1 Fuzzy version of K-means 2 Elements may belong to more than one cluster 3 Values of characteristic function range from 0 to 1. 4 It is interpreted as the degree of membership of an element to a cluster relative to all other clusters. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

30 Initial Definitions 1 Consider n objects and c clusters. 2 Each object x e X is defined by l characteristics x i = (x e1,x e2,...,x el ). 3 Consider A a set of c clusters (A = A 1,A 2,...,A c ). Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

31 C-Means Properties 1 The union of all c clusters makes the Universe i c A i = X. 2 There is no empty cluster A i X. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

32 Membership Function µ Ai (x e ) µ ie [0..1] c c µ Ai (x e ) µ ie = 1, e i=1 0 < i=1 n µ ie < n e=1 Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

33 Membership Matrix 1 Matrix containing the values of inclusion of each element into each cluster [0,1]. 2 Matrix has c (clusters) lines and n (elements) columns. 3 The sum of all elements in the column must be equal to one. 4 The sum of each line must be less than n e grater than 0. 5 No empty cluster, or cluster containing all elements. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

34 Matrix Examples 1 Two examples of clustering. 2 What do the clusters represent? e1 e2 e e4 e5 e Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

35 C-Means Algorithm v1 Arbitrarily assigns each object to a cluster (matrix U). repeat Update the cluster centres; Reassign objects to the clusters to which the objects are most similar; until no change; Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

36 C-Means Algorithm v2 Arbitrarily choose c objects as the initial cluster centres. repeat Reassign objects to the clusters to which the objects are most similar; Update the cluster centres; until no change; Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

37 Algorithm details 1 The algorithm tries to minimize the function J(U,v) = 2 m is the nebulization factor. n e=1 i=1 c µ m ie (d ie) 2 3 (d ie ) 2 is the distance between the element x e (m characteristics) and the centre of the cluster i (v i ) d ie = d(x e v i ) d ie = x e v i [ l d ie = (x ej v ij ) 2] 1/2 j=1 Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

38 Nebulization Factor 1 m is the nebulization factor. 2 This value has a range [1, ) 3 If m = 1 the the system is crisp. 4 If m then all the membership values tend to 1/c. 5 The most common values are 1.25 and 2.0 Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

39 Cluster Centres 1 The centre of the cluster i (v i ) is an l characteristics vector. 2 The jth co-ordinate is calculated as v ij = n µ m ie x ej e=1 n e=1 µ m ie Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

40 Detailed Algorithm Choose c (number of clusters). Set error (ε > 0), nebulization factor (m) and step (r = 0). Arbitrarily set matrix U(r). Do not forget, each element belongs to a single cluster, no empty cluster and no cluster has all elements. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

41 Detailed Algorithm cont. repeat Calculate the centre of the clusters vi r Calculate the distance d r i of each point to the centre of the clusters Generate U r+1 recalculating all characteristic functions. How? until U r+1 U r < ε Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

42 How to recalculate? 1 If there is any distance greater than zero then membership grade is the weighted average of the distances to all centers. 2 Otherwise the element belongs to this cluster and no other one. if d ie > 0, i [1..c] [ c then µ ie = k=1 [ die d ke ] 2 ] 1 m 1 else if d ie = 0 then µ ie = 1 else µ ie = 0 Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

43 Original x Result Data Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

44 Section Summary 1 K-Means 2 Fuzzy C-means 3 Possibilistic Clustering 4 Gustafson-Kessel Algorithm 5 Gath-Geva Algorithm 6 K-medoids Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

45 Clustering Details - I µ Ai (x e ) µ ie [0..1] c c µ Ai (x e ) µ ie > 0, e i=1 1 The membership degree is the representativity of typicality of the datum x related to the cluster i. i=1 Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

46 Algorithm details - I 1 The algorithm tries to minimize the function n c c n J(U,v) = µ m ie(d ie ) 2 + η i (1 µ ie ) m e=1 i=1 i=1 e=1 2 The first sum is the usual and the second rewards high memberships. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

47 Algorithm details - II 1 It is possible to prove that in order to minimize the function J the membership degree must be calculated as: µ ie = 1 ( ) 1 d 1+ 2 m 1 ie η i 2 Where η i = n i=1 n i=1 µ m ied 2 ie µ m ie 3 η i is a factor that controls the expansion of the cluster. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

48 Algorithm details - III 1 η i is a factor that controls the expansion of the cluster. 2 For instance, if η i is equal to the distance d 2 ie then at this distance the membership will be equal to So using η i it is possible to control the membership degrees. 4 η i is usually estimated. µ ie = 1 ( ) 1 d 1+ 2 m 1 ie η i η i = d 2 ie µ ie = 0.5 Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

49 Detailed Algorithm 1 It seems natural to repeat the algorithms K-means and C-Means that are similar. 2 However this does not give satisfactory results. 3 The algorithm tends to interpret data with low membership in all clusters as outliers instead of adjusting the results. 4 So a initial probabilistic clustering is performed. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

50 Detailed Algorithm Choose c (number of clusters). Set error (ε > 0), nebulization factor (m) and step (r = 0). Execute Algorithm Fuzzy C-Means Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

51 Detailed Algorithm cont. for 2 TIMES do Initialize U 0 and Cluster Centres with the previous results r 0 Initialize η i with the previous results repeat r r+1 Calculate the Cluster Centres using U s 1 Calculate U s until U r+1 U r < ε end for Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

52 Section Summary 1 K-Means 2 Fuzzy C-means 3 Possibilistic Clustering 4 Gustafson-Kessel Algorithm 5 Gath-Geva Algorithm 6 K-medoids Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

53 GK Method 1 This method (GK) is similar to the Fuzzy C-means (FCM). 2 The difference is the way the distance is calculated. 3 FCM uses Euclidean distances 4 GK uses Mahalanobis distances Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

54 C-Means Problem 1 C-Means and K-Means produce spherical clusters. C-Means Cluster Centre C-Means Cluster Possible better solution Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

55 Deforming Space - I 1 An arbitrary, positive definite and symmetric matrix A R p p induces a scalar product < x,y >= x T Ay [ ] 2 1/4 0 For instance the matrix A = provides 0 4 x y 2 A [ 1/4 0 = (x y) 0 4 ]( x y ) = ( x 2 /4 4y 2 ) ( (x/2) 2 = (2y) 2 ) 3 This corresponds to a deformation of the unit circle to an ellipse with a double diameter in the x-direction and a half diameter in the y-direction. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

56 Deforming Space - II 1 If there is a priori knowledge about the data elliptic clusters can be recognized. 2 If each has its own matrix the different elliptic clusters can be obtained. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

57 GK details 1 Mahalanobis distance is calculated as d 2 ik = (x i v k ) T A i (x i v k ) 2 It is possible to prove that the matrices A i are given by A i = p dets i S 1 i 3 S i is the fuzzy covariance matrix and equal to S i = n µ m ie (x e v i )(x e v i ) T e=1 Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

58 GK Comments 1 In addition to cluster centres each cluster is characterized by a symmetric and positive definite matrix A. 2 The clusters are hyper-ellipsoids on the R l. 3 The hyper-ellipsoids have approximately the same size. 4 In order to be possible to calculate S 1 the number of samples n must be at least equal to the number of dimensions l plus 1. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

59 GK Results Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

60 Section Summary 1 K-Means 2 Fuzzy C-means 3 Possibilistic Clustering 4 Gustafson-Kessel Algorithm 5 Gath-Geva Algorithm 6 K-medoids Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

61 Introduction 1 It is also known as Gaussian Mixture Decomposition. 2 It is similar to the FCM method 3 The Gauss distance is used instead of Euclidean distance. 4 The clusters no longer have a definite shape and may have various sizes. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

62 Gauss Distance 1 Gauss distance d ie = 1 P i det(ai ) exp ( ) 1 2 (x e v i ) T A 1 (x e v i ) 2 Cluster centre v i = n µ ie x e e=1 n e=1 µ ie Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

63 Gauss Distance 1 A i = n e=1 µm ie (x e v i )(x e v i ) T n e=1 µm ie 2 Probability that an element x e belongs to the cluster i P i = n e=1 µm ie n c e=1 i=1 µm ie Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

64 GG Comments 1 P i is a parameter that influences the size of a cluster. 2 Bigger clusters attract more elements. 3 The exponential term makes more difficult to avoid local minima. 4 Usually another clustering method is used to initialise the partition matrix U. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

65 GG Results Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

66 Section Summary 1 K-Means 2 Fuzzy C-means 3 Possibilistic Clustering 4 Gustafson-Kessel Algorithm 5 Gath-Geva Algorithm 6 K-medoids Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

67 Source 1 Algorithm presented in: Finding groups in Data: An Introduction to clusters analysis, L. Kaufman and P. J. Rousseeuw, John Wiley & Sons Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

68 Methods 1 K-means is sensitive to outliers since an object with an extremely large value may distort the distribution of data. 2 Instead of taking the mean value the most centrally object (medoid) is used as reference point. 3 The algorithm minimizes the sum of dissimilarities between each object and the medoid (similar to k-means) Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

69 Strategies 1 Find k-medoids arbitrarily. 2 Each remaining object is clustered with the medoid to which is the most similar. 3 Then iteratively replaces one of the medoids by a non-medoid as long as the quality of the clustering is improved. 4 The quality is measured using a cost function that measures the sum of the dissimilarities between the objects and the medoid of their cluster. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

70 Reassignment 1 Each time a reassignment occurs a difference in square-error J is contributed. 2 The cost function J calculates the total cost of replacing a current medoid by a non-medoid. 3 If the total cost is negative then m j is replaced by m random, otherwise the replacement is not accepted. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

71 Phases 1 Build phase: an initial clustering is obtained by the successive selection of representative objects until c (number of clusters) objects have been found. 2 Swap phase: an attempt to improve the set of the c representative objects is made. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

72 Build Phase 1 The first object is the one for which the sum of dissimilarities to all objects is as small as possible. 2 This is most centrally located object. 3 At each subsequent step another object that decreases the objective function is selected. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

73 Build Phase next steps I 1 Consider an object e i (candidate) which has not yet been selected. 2 Consider a non selected object e j. 3 Calculate the difference C ji, between its dissimilarity D j = d(e n,e j ) with the most similar previously selected object e n, and its dissimilarity d(e i,e j ) with object e i. 4 If C ij = D j d(e i,e j ) is positive then object e j will contribute to select object e i, C ij = max(d j d(e i,e j ),0) 5 If C ij > 0 then e j is closer to e i than any other previously selected object. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

74 Build Phase next steps II 1 Calculate the total gain G i obtained by selecting object e i G i = j (C ji ) 2 Choose the not yet selected object e i which maximizes G i = j (C ji ) 3 The process continues until c objects have been found. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

75 Swap Phase 1 It is attempted to improve the set of representative elements. 2 Consider all pairs of elements (i,h) for which e i has been selected and e h has not. 3 What is the effect of swapping e i and e h? 4 Consider the objective function as the sum of dissimilarities between each element and the most similar representative object. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

76 Swap Phase - possibility a 1 What is the effect of swapping e i and e h? 2 Consider a non selected object e j and calculate its contribution C jih to the swap: 3 If e j is more distant from both e i and e h than from one of the other representatives, e.g. e k, so C ijh = 0 4 So e j belongs to object e k, sometimes referred as the medoid m k and the swap will not change the quality of the clustering. 5 Remember: positive contributions decrease the quality of the clustering. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

77 Swap Phase - possibility a 1 Object e j belongs to medoid e k (i k). 2 If e i is replaced by e h and e j is still closer to e k, then C jih = 0. ek ej ei eh Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

78 Swap Phase - possibility b 1 If e j is not further from e i than from any one of the other representative (d(e i,e j ) = D j ), two situations must be considered: 2 e j is closer to e h than to the second closest representative e n, d(e j,e h ) < d(e j,e n ) then C jih = d(e j,e h ) d(e j,e i ). 3 Contribution C jih can either positive or negative. 4 If element e j is closer to e i than to e h the contribution is positive, the swap is not favourable. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

79 Swap Phase - possibility b.1+ 1 Object e j belongs to medoid e i. 2 If e i is replaced by e h and e j is close to e i than e h the contribution is positive, Cjih > 0. en ej ei eh Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

80 Swap Phase - possibility b.1-1 Object e j belongs to medoid e i. 2 If e i is replaced by e h and e j is not closer to e i than e h the contribution is negative. C jih<0. en ei ej eh Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

81 Swap Phase - possibility b2 1 e j is at least as distant from e h than from the second closest representative d(e j,e h ) d(e j,e n ) then C jih = d(e j,e n ) d(e j,e i ) 2 The contribution is always positive because it is not advantageous to replace e i by an e h further away from e j than from the second best closest representative object. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

82 Swap Phase - possibility b.2 1 Object e j belongs to medoid e i. 2 If e i is replaced by e h and e j is further from e h than e n, the contribution is always positive. C jih>0. eh ei ej en Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

83 Swap Phase - possibility c 1 e j is more distant from e i than from at least one of the other representative objects (e n ) but closer to e h than to any representative object, then C jih = d(e j,e h ) d(e j,e i ) ei ej eh en Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

84 Comparisons 1 K-medoids is more robust than k-means in presence of noise and outliers. 2 K-means is less costly in terms of processing time. Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

85 The End Adriano Cruz () Cluster Algorithms 28 de outubro de / 80

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