1. A Cumulative Voting Consensus Method for Partitions with a Variable Number of Clusters Hanan G. Ayad, Mohamed S. Kamel, ECE Department University of Waterloo, Ontario, Canada Hamdi JENZRI MRL Seminar

2. Outline  Introduction  Consensus Clustering  Review of consensus methods  Contribution of the authors  Theoretical formulation  Algorithms  Experimental results  Conclusion 2

3. Introduction  Cluster Analysis: discovery of a meaningful grouping for a set of data objects by finding a partition that optimizes an objective function  The number of ways of partitioning a set of n objects into k non empty clusters is a Stirling set number of the second kind, which is of the order of k n /k!  A well-known dilemma in data clustering is the multitude of models for the same set of data objects,  Different algorithms,  Different distance measures,  Different features for characterizing the objects,  Different scales (the number of clusters)...  This issue led to a lot of research work that addressed the problem of comparison and consensus of data clustering 3

4. Consensus Clustering  Consensus Clustering (Cluster ensembles): Finding a consensus partition that summarizes an ensemble of b partitions in some meaningful sense … Data Method 1 Method 2 Method b … 4

5. Objectives of Consensus Clustering  Improving clustering accuracy over a single data clustering,  Allowing the discovery of arbitrarily shaped cluster structures,  Reducing the instability of a clustering algorithm due to noise, outliers, or randomized algorithms,  Reusing preexisting clusterings (knowledge reuse),  Exploring random feature subspaces or random projections for high-dimensional data,  Exploiting weak clusterings such as splitting the data with random hyperplanes,  Estimating confidence in cluster assignments for individual observations,  Clustering in distributed environments including feature or object distributed clustering. 5

6. Consensus Approaches  Axiomatic: is concerned with deriving possibility / impossibility theorems on the existence and uniqueness of consensus partitions satisfying certain conditions.  Constructive: specifies rules for constructing a consensus, such as the Pareto rule, also known as the strict consensus rule, whereby two objects occur together in a consensus if and only if they occur together in all the individual partitions.  Combinatorial optimization: considers an objective function J, measuring the remoteness of a partition to the ensemble of partitions, and searches for a partition in the set of all possible partitions of the data objects that minimizes J. The approach is related to the notion of central value in statistics. 6

7. Challenge in Consensus Clustering  The purely symbolic nature of the labels returned by the clustering algorithms  Let’s take the example of partitioning a 7-objects data set into k= 3 clusters, where the first 3 objects belong to a first cluster, the following 2 objects to a second cluster and the remaining 2 objects to a third cluster:  The vector representation of the clustering result can be:  [1 1 1 2 2 3 3], [2 2 2 1 1 3 3], [3 3 3 2 2 1 1] or any of the k! = 3! = 6 possible label permutations  The matrix representation of the same partition can be: ,, or any of the k! = 3! = 6 possible permutations of the rows 1 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 1 1 1 0 0 0 0 7

8. Review of Consensus Methods AuthorsAlgorithmDescription Computational Complexity Disadvantages Strehl and Ghosh Cluster-based Similarity Partitioning Algorithm (CSPA) The optimal consensus is the partition that shares the most information with the ensemble of partitions, as measured by the Average Normalized Mutual Information (ANMI) Quadratic in n They seek balanced size clusters, making them unsuitable for data with highly unbalanced clusters Hyper Graph Partitioning Algorithm (HGPA) Linear in n Meta CLustering Algorithm (MCLA) 8

9. Review of Consensus Methods AuthorsAlgorithmDescriptionComputational Complexity Fred and Jain Evidence Accumulation Clustering (EAC) The cluster ensemble is mapped to a co-association matrix, where entries can be interpreted as votes (or vote ratios) on the pairwise co- occurrences of objects and are computed as the number of times each pair of objects co- occurs in the same cluster of a base clustering (relative to the total number of base clusterings). The final consensus clustering is extracted by applying linkage- based clustering algorithms on the co-association matrix. Quadratic in n 9

10. Contributions of the Authors  They introduce a new solution for the problem of aligning the cluster labels of a given clustering with k i clusters with respect to a reference clustering with k o clusters.  This is done through what they call “cumulative voting”  Plurality voting scheme (winner takes all) allows each voter to vote for one option, and the option that receives the most votes is the winner.  Cumulative voting is a rated voting scheme, where each voter gives a numeric value (called rating) to each option such that the voter’s ratings add up to a certain total (for example, a number of points).  Cumulative voting is sometimes referred to as weighted voting.  As proposed in this paper, cumulative voting maps an input k i -partition into a probabilistic representation as a k o -partition with cluster labels corresponding to the labels of the reference k o clusters.  They formulate the selection criterion for the reference clustering based on the maximum information content, as measured by the entropy. 10

11. Contributions of the Authors  They explore different cumulative voting models  with a fixed reference partition  Un-normalized weighting  Normalized weighting  with an adaptive reference partition, the reference partition is incrementally updated so as to relax the dependence on the selected reference. Furthermore, these updates are performed in a decreasing order of entropies so as to smooth the updates of the reference partitions.  Based on the proposed cumulative vote mapping, they define the criterion for obtaining a first summary of the ensemble as the minimum average squared distance between the mapped partitions and the optimal representation of the ensemble, 11

12. Contributions of the Authors  Finally, they formulate the problem of extracting the optimal consensus partition as that of finding a compressed summary of the estimated distribution that preserves the maximum relevant information.  They relate the problem to the Information Bottleneck (IB) method of Tishby et al. and propose an efficient solution using an agglomerative algorithm similar to the agglomerative IB algorithm of Slonim and Tishby, which minimizes the Jensen- Shannon (JS) divergence within the cluster.  They demonstrate the effectiveness of the proposed consensus algorithms using ensembles of K-Means clusterings with a randomly selected number of clusters, where the goal is to enable the discovery of arbitrary cluster structures. 12

13. Architecture of the proposed Method Minimizing the Jensen- Shannon (JS) divergence Minimum average squared distance Cumulative Vote Mapping Maximum entropy Selection of the Reference Partition Un-normalized Vote Weighting Scheme Normalized Vote Weighting Scheme First Summary of the partitions Finding the optimal consensus Adaptive Vote Weighting Scheme 13

14. Theoretical Formulation  Let X denote a set of n data objects x j, where x j R d  Partition of X into k clusters is represented as an n-dimentional cluster labeling vector y C n, where C = {c 1,..., c k }  Alternatively, the partition can be represented as a k x n matrix denoted as U with a row for each cluster and a column for each object x j  Hard partition, U is referred to as a binary stochastic matrix, where each entry u lj {0, 1}, and  Soft partition, let C denote a categorical random variable defined over the set of cluster labels C, a stochastic partition corresponds to a probabilistic clustering and is defined as a partition where each observation is assigned to a cluster by an estimated posterior probability 14

15. Theoretical Formulation  Consider as input an ensemble of b partitions with a variable number of clusters  U = {U 1, …, U b }, such that each partition U i is a k i x n binary stochastic matrix (hard partitions)  They use a center-based clustering algorithm, namely the K-Means for text data to generate the cluster ensembles.  The number of clusters for individual partitions is randomly selected within some range k i [k min, k max ]  They address the problem of estimating a consensus partition for the set of data objects X that optimally represents the ensemble U 15

16. Selection of the reference partition  Consider the random variable C i defined over the cluster labels of the ith clustering, with probability distribution where is the number of objects assigned to cluster  The Shannon entropy, defined as a measure of the average information content (uncertainty) associated with a random outcome is a function of its distribution  The higher the entropy, the more informative is the distribution  Thus, the partition U i U with the highest entropy represents the cluster label distribution with the maximum information content: 16

17. Cumulative Vote Mapping  Consider some reference partition U o and a partition U i U with k o and k i clusters and associated random variables C o and C i with estimated probability distributions denoted as  U i is designated as the voting partition with respect to U o  Each cluster is viewed as a voter that votes for each of the clusters, with a weight denoted as  The weights are represented in a k o x k i cumulative vote weight matrix, denoted as  Un-normalized weighting scheme  Normalized weighting scheme 17

18. Un-normalized Weighting Scheme  It is connected to the co-association matrix commomly used for summarizing a set of partitions  Let and be the qth and lth row vectors of U o and U i respectively  Represents the number of objects belonging to both clusters dv and  The binary k i vectors of U i are transformed into k o frequency vectors represented in the mapped matrix U o,i  Members of cluster are scaled by when mapped as members of clusters 18

19. Un-normalized Weighting Scheme  Each entry of the k o x n matrix U o,i is taken as an assignment frequency of object x j to cluster where  Example: 19

20. Normalized Weighting Scheme  Normalizing the weights to sum to 1  The weight is computed as the average of the conditional probabilities of cluster, given each of the data objects assigned to cluster, which is taken as an estimate of the conditional probability. When the reference partition is represented as a binary stochastic matrix..  Each of the k i columns of is a probability vector 20

21. Normalized Weighting Scheme  Each partition U i is mapped using:  Each entry of U o,i is considered as an estimate of  U o,i is a stochastic partition representing  Consider  The estimated priors 21

22. Normalized Weighting Scheme  Which gives:  Which ensures the entropy preserving property  The normalization scheme reflects the intuition that objects that are members of a large cluster are considered less strongly associated to each other than objects belonging to a small cluster 22

23. Normalized Weighting Scheme  Example 23

24. Average-Squared-Distance Criterion for Mapped Partitions  The chosen criterion for finding a stochastic partition Û of X summarizing a set of b partitions as the minimum average distance between the mapped ensemble partitions and the optimal consensus is defined as follows:  Where represents the mapping of partition U i into the stochastic partition U o,i, defined with respect to the reference partition U o  The dissimilarity function h() is defined as the average squared distance between the probability (column) vectors fd and and given as 24

25. Average-Squared-Distance Criterion for Mapped Partitions  This minimization problem can be solved directly by calculating as the average of the probability vectors  It’s to note that using the cumulative vote mapping of the partitions, the number of clusters of Û is preset through the selected reference partition, regardless of the number of clusters k i of each partition. 25

26. Algorithms  Un-normalized Reference-based cumulative Voting (URCV) 26

27. Algorithms  Reference-based cumulative voting (RCV) 27

28. Algorithms  Adaptive cumulative voting (ACV) 28

29. Finding the optimal consensus  The above summarization of the ensemble do not always lead to capturing the most “meaningful” or “relevant” information, given the arbitrary number of clusters k i  The problem of extracting a compressed representation of stochastic data that captures only the relevant or meaningful information was addressed by the Informarion Bottleneck (IB) method of Tishby et al.  It addresses a trade-off between compressing the representation and preserving meaningful information.  In this paper, they formulate a subsequent problem as that of finding an efficient representation of random variable C, described by random variable Z, that preserves the maximum amount of relevant information about X, based on the estimated distribution 29

30. Finding the optimal consensus  Solution that is approximately equivalent to the AIB algorithm but requires less computational time  They map the k o clusters to a (k o ) 2 JS divergence matrix  They apply a distance-based clustering algorithm  Agglomerative group average algorithm because it minimizes the average pairwise distances between members of the merged clusters, as given by its objective function  Where S1 and S2 denote a pair of distributions, whose cardinalities are |S 1 | and |S 2 |, respectively 30

31. Finding the optimal consensus  The JS divergence is the entropy of the weighted average distribution minus the weighted average of the entropies of the individual distributions and. It is symmetric, bounded, nonnegative, and equal to zero when. 31

32. Finding the optimal consensus  When a k-partition {S 1, …, S k } is obtained, the consensus clustering Z k described by estimates of the prior probabilities of the consensus clusters and the posterior probabilities are computed using 32

33. Finding the optimal consensus 33

34. Case of identical partitions  An essential property for consensus clustering algorithms is that when all individual partitions represent a perfect consensus, that is, they are identical with respect to cluster label permutations, the consensus solution should be the same partition.  In the algorithms presented in this paper, this property is satisfied.  In the case of the normalized weighting scheme becomes the identity matrix I  In the case of the un-normalized weighting scheme, is a diagonal matrix whose qth diagonal element equals to. Each entry of U o,i is equal to the size of the corresponding cluster. After averaging we get the exact same partition started with. 34

35. Experimental Setup  They compare the performances of the URCV, RCV, and ACV algorithms with several recent consensus algorithms and with the average quality of the ensemble partitions  External Validation: Adjusted Rand Index with respect to the true clustering  They report the distribution of the Adjusted Rand Index using boxplots for r =20 runs for small data sets and for r=5 runs for large data sets (n ≥ 1000)  Internal Validation: they measure the Normalized Mutual Information (NMI) between every pair of consensus clusterings over multiple runs of the proposed consensus algorithms (interconsensus NMI)  To assess the stability of the consensus clustering over multiple runs  Variations in the consensus clustering across multiple runs are due to the ensemble partitions being generated with a random number of clusters and with different random seeds for the K-Means algorithm.  Default value for b = 25 35

36. Experimental Setup 36

37. Algorithms compared to  The binary one-to-one voting algorithm of Dimitriadou et al. (BVA) 37

38. Data sets 38

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46. Computational Complexity AlgorithmComplexity Co-association based algorithmsO(n 2 b) CSPAO(n 2 kb) MCLAO(nk 2 b 2 ) HGPAO(nkb) QMIO(nkb) URCV, RCV, ACVO(nk o 2 b) 46

47. Conclusion  Cumulative voting to map an input k i -partition to a reference k o -partition with probabilistic assignments  Un-normalized  Normalized  The reference partition was chosen as the one having the maximum entropy  Minimum average distance criterion between the mapped ensemble partitions and the summarizing stochastic partition Û  Extracting the optimal consensus partition from Û by minimizing the JS divergence within the cluster  Over all, the proposed methods performed better than the existing consensus methods, with less complexity 47