Information based clustering

Details Information based clustering Information based clustering

2005-11-26

arxiv.org/abs/q-bio/0511043v1

Biology

Quantitative Biology

Quantitative Methods

To appear in Proceedings of the National Academy of Sciences USA, 11 pages, 9 figures

Scientific paper

10.1073/pnas.0507432102

In an age of increasingly large data sets, investigators in many different disciplines have turned to clustering as a tool for data analysis and exploration. Existing clustering methods, however, typically depend on several nontrivial assumptions about the structure of data. Here we reformulate the clustering problem from an information theoretic perspective which avoids many of these assumptions. In particular, our formulation obviates the need for defining a cluster "prototype", does not require an a priori similarity metric, is invariant to changes in the representation of the data, and naturally captures non-linear relations. We apply this approach to different domains and find that it consistently produces clusters that are more coherent than those extracted by existing algorithms. Finally, our approach provides a way of clustering based on collective notions of similarity rather than the traditional pairwise measures.

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