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Issue No.07 - July (2014 vol.36)
pp: 1340-1353
and Michael I. Jordan , Computer Science Division and Department of Statistics, University of California, 387 Soda Hall 1776, Berkeley ,
Complex data can be grouped and interpreted in many different ways. Most existing clustering algorithms, however, only find one clustering solution, and provide little guidance to data analysts who may not be satisfied with that single clustering and may wish to explore alternatives. We introduce a novel approach that provides several clustering solutions to the user for the purposes of exploratory data analysis. Our approach additionally captures the notion that alternative clusterings may reside in different subspaces (or views). We present an algorithm that simultaneously finds these subspaces and the corresponding clusterings. The algorithm is based on an optimization procedure that incorporates terms for cluster quality and novelty relative to previously discovered clustering solutions. We present a range of experiments that compare our approach to alternatives and explore the connections between simultaneous and iterative modes of discovery of multiple clusterings.
Kernel, Clustering algorithms, Optimization, Correlation, Labeling, Algorithm design and analysis, Vectors,dimensionality reduction, Kernel methods, non-redundant clustering, alternative clustering, multiple clustering
and Michael I. Jordan, "Iterative Discovery of Multiple AlternativeClustering Views", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.36, no. 7, pp. 1340-1353, July 2014, doi:10.1109/TPAMI.2013.180
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