Information-based clustering

N. Slonim, G. Atwal, G. Tkacik, W. Bialek, PNAS 102 (2005) 18297–18302.


Journal Article | Published
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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.
Publishing Year
Date Published
2005-12-20
Journal Title
PNAS
Volume
102
Issue
51
Page
18297 - 18302
IST-REx-ID

Cite this

Slonim N, Atwal G, Tkacik G, Bialek W. Information-based clustering. PNAS. 2005;102(51):18297-18302. doi:10.1073/pnas.0507432102
Slonim, N., Atwal, G., Tkacik, G., & Bialek, W. (2005). Information-based clustering. PNAS, 102(51), 18297–18302. https://doi.org/10.1073/pnas.0507432102
Slonim, N., G. Atwal, Gasper Tkacik, and William Bialek. “Information-Based Clustering.” PNAS 102, no. 51 (2005): 18297–302. https://doi.org/10.1073/pnas.0507432102.
N. Slonim, G. Atwal, G. Tkacik, and W. Bialek, “Information-based clustering,” PNAS, vol. 102, no. 51, pp. 18297–18302, 2005.
Slonim N, Atwal G, Tkacik G, Bialek W. 2005. Information-based clustering. PNAS. 102(51), 18297–18302.
Slonim, N., et al. “Information-Based Clustering.” PNAS, vol. 102, no. 51, National Academy of Sciences, 2005, pp. 18297–302, doi:10.1073/pnas.0507432102.

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