Combining multiple clusterings using evidence accumulation

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 27; no. 6; pp. 835 - 850
• Main Authors: Fred, Ana L.N., Jain, Anil K.
• Format: Journal Article
• Language: English
• Published: Los Alamitos, CA IEEE 01.06.2005; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agglomeration; Algorithm design and analysis; Algorithms; Applied sciences; Artificial Intelligence; Breast Neoplasms - diagnosis; Cluster Analysis; cluster fusion; cluster validity; Clustering; Clustering algorithms; Clusters; combining clustering partitions; Computer science; control theory; systems; Computer Simulation; evidence accumulation; Exact sciences and technology; Feature extraction; Humans; Image Interpretation, Computer-Assisted - methods; Information Storage and Retrieval - methods; K-means algorithm; Models, Biological; Models, Statistical; Mutual information; Partitioning algorithms; Partitions; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Representations; robust clustering; Robustness; Shape; Similarity; single-link method; Strategy; Cluster analysis; Partition; Cluster; K means algorithm; cluster fusion; cluster validity; Validity; K-means algorithm; single-link method; evidence accumulation; robust clustering; Mutual information; combining clustering partitions
• ISSN: 0162-8828; 2160-9292; 1939-3539
• DOI: 10.1109/TPAMI.2005.113

We explore the idea of evidence accumulation (EAC) for combining the results of multiple clusterings. First, a clustering ensemble - a set of object partitions, is produced. Given a data set (n objects or patterns in d dimensions), different ways of producing data partitions are: 1) applying different clustering algorithms and 2) applying the same clustering algorithm with different values of parameters or initializations. Further, combinations of different data representations (feature spaces) and clustering algorithms can also provide a multitude of significantly different data partitionings. We propose a simple framework for extracting a consistent clustering, given the various partitions in a clustering ensemble. According to the EAC concept, each partition is viewed as an independent evidence of data organization, individual data partitions being combined, based on a voting mechanism, to generate a new n × n similarity matrix between the n patterns. The final data partition of the n patterns is obtained by applying a hierarchical agglomerative clustering algorithm on this matrix. We have developed a theoretical framework for the analysis of the proposed clustering combination strategy and its evaluation, based on the concept of mutual information between data partitions. Stability of the results is evaluated using bootstrapping techniques. A detailed discussion of an evidence accumulation-based clustering algorithm, using a split and merge strategy based on the k-means clustering algorithm, is presented. Experimental results of the proposed method on several synthetic and real data sets are compared with other combination strategies, and with individual clustering results produced by well-known clustering algorithms.