Information-based clustering.
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.
Slonim
Noam
N
Joseph Henry Laboratories of Physics, and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA. nslonim@princeton.edu
Atwal
Gurinder Singh
GS
Tkacik
Gasper
G
Bialek
William
W
D000465:Algorithms; D016000:Cluster Analysis; D004777:Environment; D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal; D012441:Saccharomyces cerevisiae / Q000235:genetics