Information-based clustering. Author Noam Slonim, Gurinder Atwal, Gašper Tkačik, William Bialek Publication Year 2005 Type Journal Article Abstract 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 that 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 nonlinear 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. Keywords Gene Expression Profiling, Gene Expression Regulation, Fungal, Saccharomyces cerevisiae, Cluster Analysis, Algorithms, Environment Journal Proc Natl Acad Sci U S A Volume 102 Issue 51 Pages 18297-302 Date Published 12/2005 Alternate Journal Proc. Natl. Acad. Sci. U.S.A. Google ScholarBibTeXEndNote X3 XML