1. Chapter 9 UNSUPERVISED LEARNING: Clustering Part 2 Cios / Pedrycz / Swiniarski / Kurgan

2. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 2 Some key observed features in the operation of a human associative memory: information is retrieved/recalled from the memory on basis of some measure of similarity relating to a key pattern memory is able to store and recall representations as structured sequences the recalls of information from memory are dynamic and similar to time-continuous physical systems SOM Clustering

3. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 3

4. 4

5. 5 Self-Organizing Feature Maps In data analysis it is fundamental to: capture the topology and probability distribution of pattern vectors map pattern vectors from the original high-D space onto the lower-D new feature space (compressed)

6. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 6 Data compression requires selection of features that best represent data for a specific purpose, e.g., better visual inspection of the data's structure Most attractive from the human point of view are visualizations in 2D or 3D The major difficulty is faithful projection/mapping of data to ensure preservation of the topology present in the original feature space. Self-Organizing Feature Maps

7. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 7 Topology-preserving mapping should have these properties: similar patterns in the original feature space must also be similar in the reduced feature space - according to some similarity criteria similarity in the original, and the reduced spaces, should be of "continuous nature “ i.e., density of patterns in the reduced feature space should correspond to those in the original space. Self-Organizing Feature Maps

8. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 8 Several methods were developed for 2D topology- preserving mapping: linear projections, such as eigenvectors nonlinear projections, such as Sammon's projection nonlinear projections, such as SOM neural networks Self-Organizing Feature Maps

9. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 9 Sammon’s Projection

10. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 10 Sammon’s Projection Performs a non-linear projection, typically, onto a 2D plane Disadvantages: -it is computationally heavy -it cannot be used to project new points (points that were not used during training) on the output plane

11. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 11 SOM: Principle high-dim space low-dim space (2-dim, 3-dim)

12. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 12 Developed by Kohonen in 1982 SOM is an unsupervised learning, topology preserving, projection algorithm It uses a feedforward topology It is a scaling method projecting data from high-D input space into a lower-D output space Similar vectors in the input space are projected onto nearby neurons on the 2D map Self-Organizing Feature Maps

13. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 13 The feature map is a layer in which the neurons are self-organizing themselves, according to input values Each neuron of the input layer is connected to each neuron of the 2D topology/map The weights associated with the inputs are used to propagate them to the map neurons Self-Organizing Feature Maps

14. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 14 The neurons in a certain area around the winning neuron are also influenced SOM reflects the ability of biological neurons to perform global ordering based on local interactions SOM: Topology and Learning Rule

15. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 15 One iteration of the SOM learning: 1.Present a randomly selected input vector x to all neurons 2.Select the winning neuron, i.e., one whose weight vector is closest to the input vector, according to the chosen similarity measure 3.Adjust the weight of the jth winning neuron, and the weights of neighboring (defined by some neighborhood function) neurons SOM: Topology and Learning Rule

16. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 16 The jth winning neuron is selected as the one having minimal distance value: Competitive (winner-takes-all / Kohonen) learning rule is used for adjusting the weights: SOM: Topology and Learning Rule

17. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 17 Kohonen also proposed a dot product similarity for selecting the winning neuron: and the learning rule: where N j (t) is the winning neuron ’ s neighborhood and (0<  (t) <  ) is the decreasing learning function. This formula assures automatic weight normalization to the length of one. SOM: Topology and Learning Rule

18. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 18

19. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 19 The neighborhood kernel (of the winning neuron) is a non-increasing function of time and distance It defines the region of influence that the input has on the SOM Neighborhood Kernel

20. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 20 The geometric set of neurons must decrease with the increase of iteration steps (time) Convergence of the learning process requires that the radius must decrease with learning time/iteration This causes global ordering by local interactions and local weight adjustments. Neighborhood Kernel

21. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 21

22. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 22 With the neighborhood function fixed, the neighborhood kernel learning function can be defined as: Neighborhood Kernel

23. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 23 Another frequently used neighborhood kernel is a Gaussian function with a radius decreasing with time: Neighborhood Kernel

24. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 24 Conditions for successful learning of the SOM network: the training data must be large since self-organization relies on statistical properties of data proper selection of the neighborhood kernel function assures that only the weights of the winning neuron and its neighborhood neurons are locally adjusted the radius of the winning neighborhood, as well as the learning function rate, must monotonically decrease with time the amount of weight adjustment for neurons in a winning neighborhood depends on how close they are to the winner SOM: Algorithm

25. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 25 Given: The 2D network topology consisting of M neurons; training data set of L n-D input vectors; number of iterations T; neighborhood kernel function; learning rate function 1. Set learning rate to the max learning rate function 2. set iteration step t=0 3. randomly select initial values of the weights 4. randomly select the input pattern and present it to the network 5. compute the current learning rate for step t using the given learning rate function SOM: Algorithm

26. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 26 6. compute the Euclidean distances || x i - w k (t) ||, k = 1, …, M 7. select the jth winning neuron || x i - w j (t) || = min || x i (t) – w k (t) ||, k = 1, …, M 8. define the winning neighborhood around the winning neuron using the neighborhood kernel 9. adjust the weights of the neurons w p (t+1) = w p (t) +  (t) (x i – w p (t)), p  N j (t) 10. increase t=t+1 If t>T stop; otherwise go to step 4 Result: Trained SOM network SOM: Algorithm

27. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 27 from Kohonen

28. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 28 Given: Trained SOM network consisting of the 2D array of M neurons, each neuron receiving an input via its weight vector w. Small training data set consisting of pairs (x i, c i ), i=1, 2, …, L, where c is the class label 1. Set i = 1 2. Present input pattern to the network 3. Calculate the distances or the dot products 4. Locate the spatial position of the winning neuron and assign label c to that neuron 5. Increase i= i+1 and continue with i < = L Result: Calibrated SOM network SOM: Interpretation

29. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 29 In practice, we do not optimize the SOM design, instead, we pay attention to these factors: Initialization of the weights Often they are normalized, and can be set equal to the first several input vectors. If they are not initialized to some input vectors then they should be grouped in one quadrant of the unit circle so that they can unfold to map the distribution of input data. Starting value of the learning rate and its decreasing schedule Structure of neighborhood kernel and its decreasing schedule SOM: Issues

30. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 30 SOM: Example

31. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 31 SOM: Example

32. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 32 SOM: Example

33. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 33 SOM: Example

34. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 34 Self-Organizing Feature Map (SOM) Visualize a structure of highly dimensional data by mapping it onto the low-dimensional (typically two-dimensional) grid of linear neurons

35. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 35 Clustering and Vector Quantization

36. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 36 Cluster validity Using different clustering methods might result in different partitions of X, at each value of c Which clusterings are valid? It is plausible to expect “good” clusters at more than one value of c ( 2  c < n ) How many clusters do exist in the data?

37. Cluster validity Some Symbol X : {X1,X2,…,Xn} U = {Uik} : c partitions of X are sets of (cn) values {Uik} that can be conveniently array. There are three sets of partition matrices © 2007 Cios / Pedrycz / Swiniarski / Kurgan 37

38. Cluster Validity classification of validity measures Direct Measure: Davies-Bouldin Index, Dunn’s index Indirect measures for fuzzy clusters: degree of separation, partition coefficient and partition entropy. Xie and Beni index © 2007 Cios / Pedrycz / Swiniarski / Kurgan 38

39. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 39 Cluster validity Cluster Error is associated with any U  M c it is the number of vectors in X that are mislabeled by U E(U) is an absolute measure of cluster validity when X is labeled, and is undefined when X is not labeled.

40. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 40 Cluster validity Process X at c = 2, 3, …, n – 1 and record the optimal values of some criterion as a function of c The most valid clustering is taken as an extremum of this function (or some derivative of it) Problem: many criterion functions usually have multiple local stationary points at fixed c, and global extrema are not necessarily the “best” c-partitions of the data

41. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 41 Cluster validity More formal approach is to pose the validity question in the framework of statistical hypothesis testing Major difficulty is that the sampling distribution is not known Nonetheless, goodness of fit statistics such as chi-square and Kolmogorov-Smirnov tests have been used

42. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 42 Cluster validity The global minimum of J w may suggest the “wrong” 2- clusters Example from Bezdek: n=29 data vectors {x k }  R 2 “correct” 2-partition of X is shown on the left The global minimum is hardly an attractive solution. X s For hard 2-partition,

43. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 43 Cluster validity Basic question: what constitutes a “good” cluster ? What is a “cluster” anyhow? The difficulty is that the data X, and every partition of X, are separated by the algorithm generating partition matrix U (and defining “clusters” in the process)

44. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 44 Cluster validity Many of the algorithms ignore this fundamental difficulty and are thus heuristic in nature Some heuristic methods measure the amount of fuzziness in U, and presume the least fuzzy partitions to be the most valid

45. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 45 Degree of Separation - Bezdek The degree of separation between fuzzy sets u 1 and u 2 is the scalar and its generalization from 2 to c clusters is:

46. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 46 Degree of Separation - Bezdek Example: c=2 and two different fuzzy 2-partitions of X: Z(U;2) = Z(V;2) = 0.50 U and V are very different so Z does not distinguish between the two partitions.

47. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 47 Partition Coefficient - Bezdek U is a fuzzy c-partition of n data points. The partition coefficient, F, of U, is the scalar: The value of F(U;c) depends on all (c x n) elements of U in contrast to Z(U;c) that depends on just one.

48. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 48 Partition Coefficient - Bezdek Example: values of F on U and V partitions of X: F(U;2) = 0.510 F(V; 2) = 0.990 The value of F gives accurate indication of the partition, for both the most uncertain and certain states.

49. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 49 Partition Coefficient - Bezdek from Bezdek

50. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 50 Partition Coefficient - Bezdek Values of F(U;c) for c = 2, 3, 4, 5, 6 with the norms N E, N D and N M F first identifies a primary structure at c * = 2; and then the secondary structure at c = 3 Norm cNENE NDND NMNM 20.880.890.56 30.780.800.46 40.71 0.36 50.660.680.31 60.600.620.37

51. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 51 Partition Entropy - Bezdek Let {A i | 1  i  c } denote a c-partition of events of any sample space connected with an experiment; and let p T = (p 1,p 2, …, p c ) denote a probability vector associated with the {A i }. The pair ({A i }, p) is called a finite probability scheme for the experiment i th component of p is the probability of event A i c is called the length of the scheme Note: c does NOT indicate the number of clusters

52. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 52 Partition Entropy - Bezdek Our aim is to find a measure h(p) of the amount of uncertainty associated with each state. h(p) – should maximize for p=(1/c, …, 1/c) h(p) – should minimize for p=(0, 1, 0, …) (any partition statistically certain)

53. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 53 Partition Entropy - Bezdek The entropy of the scheme is defined as: p i log a (p i ) = 0 whenever p i = 0

54. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 54 Partition Entropy - Bezdek Partition entropy of any fuzzy c-partition U  M fc of X, where |X| = n, is, for 1  c  n

55. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 55 Partition Entropy - Bezdek Let U  M fc be a fuzzy c-partition of n data points. Then for 1  c  n and a  (1,  ) 0  H(U;c)  log a (c) H(U;c) = 0  M co is hard H(U;c) = log a (c)  U = [1/c]

56. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 56 Partition Entropy - Bezdek Example: entropy for U and V H(U;c) = 49 log e (2)/51 = 0.665 H(V;c) = log e (2)/51 = 0.013 U is a very uncertain partition

57. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 57 Partition Entropy - Bezdek We assume that minimization of H(U;c) corresponds to maximizing the amount of information about structural memberships an algorithm extracted from data X. H(U;c) is used for cluster validity as follows:  c denotes a finite set of “optimal” U’s  M fc c = 2, 3, …, n-1

58. Partition Entropy - Bezdek Normalized partition entropy: Reasons: Variable ranges make interpretation of values of Vpc and Vpe difficult. Since they are not referenced to a fixed scale. For example © 2007 Cios / Pedrycz / Swiniarski / Kurgan 58

59. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 59 Partition Entropy - Bezdek Normalized partition entropy:

60. Cluster Validity Comment for partition coefficient and partition entropy Vpc maximizes (and Vpe minimizes) on every crisp c-partition of X. And at the other extreme, Vpc takes its unique minimum( and Vpe takes its unique maximum) at the centroid U =[1/c]=. The “fuzziest” partition you can get since it assigns every point in X to all c classes with equal membership values 1/c. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 60

61. Cluster Validity Comment for partition coefficient and partition entropy Vpc and Vpe essentially measure the distance U is from being crisp by measuring the fuzziness in the rows of U All these two indices really measures is fuzziness relative to partitions that yield other values of the indices There are roughly ( ) crisp matrices in Mhcn and Vpc is constantly 1 © 2007 Cios / Pedrycz / Swiniarski / Kurgan 61

62. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 62 from Bezdek

63. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 63 Prototypes for FEATURE SELECTION from Bezdek Symptom Feature centers Absolute differences (Hernia) v 1j (Gallstones) v 2j 10.570.270.30 20.980.670.31 30.060.93  0.87 40.220.550.33 50.170.100.07 60.770.840.07 70.420.050.37 80.390.840.45 90.480.040.44 100.020.160.14 110.120.250.13

64. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 64 Cluster Errors for FEATURE SELECTION from Bezdek Symptoms used Cluster Error E(U) {1-11}23 {3}23 {3, 8}23 {3, 9}36 {3, 8, 9}36

65. Cluster Validity Example © 2007 Cios / Pedrycz / Swiniarski / Kurgan 65

66. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 66 Cluster Validity Divisive Coefficient (DC) a,b,c, d,e c,d,e d,e a b c d e a,b 10.05.03.02.00.0

67. Cluster Validity Divisive Coefficient For each object i, let d ( i ) denote the diameter of the last cluster to which it belongs (before being split off as a single object), divided by the diameter of the whole data set. The divisive coefficient (DC), given by DC=( © 2007 Cios / Pedrycz / Swiniarski / Kurgan 67

68. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 68 Cluster Validity C- number of clusters being included

69. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 69 Cluster Validity Divisive Coefficient (DC) a,b,c, d,e c,d,e d,e a b c d e a,b 10.05.03.02.00.0 l0l0 l1l1 l2l2 l4l4 l3l3 l5l5 l6l6 l7l7 l8l8 L1=(1-((10-5)/10))3=1.5

70. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 70 Cluster Validity Divisive Coefficient (DC) a,b,c, d,e c,d,e d,e a b c d e a,b 10.05.03.02.00.0 l0l0 l1l1 l2l2 l4l4 l3l3 l5l5 l6l6 l7l7 l8l8

71. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 71 Cluster Validity Divisive Coefficient (DC) a,b,c, d,e c,d,e d,e a b c d e a,b 10.05.03.02.00.0 l0l0 l1l1 l2l2 l4l4 l3l3 l5l5 l6l6 l7l7 l8l8

72. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 72 Cluster Validity Divisive Coefficient (DC) a,b,c, d,e c,d,e d,e a b c d e a,b 10.05.03.02.00.0 l0l0 l1l1 l2l2 l4l4 l3l3 l5l5 l6l6 l7l7 l8l8

73. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 73 Cluster Validity Divisive Coefficient (DC) a,b,c, d,e c,d,e d,e a b c d e a,b 10.05.03.02.00.0 l0l0 l1l1 l2l2 l4l4 l3l3 l5l5 l6l6 l7l7 l8l8

74. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 74 Cluster Validity How to assess the quality of clusters? How many clusters should be found distinguished in data?

75. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 75 Cluster Validity: Davies-Bouldin index

76. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 76 Cluster Validity: Dunn separation index

77. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 77 Cluster Validity: Xie-Benie achieve the lowest value of “r”

78. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 78 Cluster Validity: Fuzzy Clustering Partition coefficient Partition entropy Sugeno-Takagi

79. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 79 Random Sampling Two-phase clustering: (a) Random sampling and (b) Clustering of prototypes

80. © 2007 Cios / Pedrycz / Swiniarski / Kurgan 80 References