Data Clustering Using Evidence Accumulation

We explore the idea of evidence accumulation for combining the results of multiple clusterings. Initially, n d-dimensional data is decomposed into a large number of compact clusters; the K-means algorithm performs this decomposition, with several clusterings obtained by N random initializations of the K-means. Taking the cooccurrences of pairs of patterns in the same cluster as votes for their association, the data partitions are mapped into a co-association matrix of patterns. This n ? n matrix represents a new similarity measure between patterns. The final clusters are obtained by applying a MST-based clustering algorithm on this matrix. Results on both synthetic and real data show the ability of the method to identify arbitrary shaped clusters in multidimensional data.

• Type: Conference
• Year: 2002
• Where: ICPR
• Authors: Ana L. N. Fred, Anil K. Jain
• Keywords: Arbitrary Shaped Clusters, Computer Vision, ICPR 2002, K-means Algorithm Performs, MST-based Clustering Algorithm