Macrostate data clustering.

Daniel Korenblum; David Shalloway

doi:10.1103/physreve.67.056704

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Macrostate data clustering.

Daniel Korenblum·David Shalloway

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Abstract

We develop an effective nonhierarchical data clustering method using an analogy to the dynamic coarse graining of a stochastic system. Analyzing the eigensystem of an interitem transition matrix identifies fuzzy clusters corresponding to the metastable macroscopic states (macrostates) of a diffusive system. A "minimum uncertainty criterion" determines the linear transformation from eigenvectors to cluster-defining window functions. Eigenspectrum gap and cluster certainty conditions identify the proper number of clusters. The physically motivated fuzzy representation and associated uncertainty analysis distinguishes macrostate clustering from spectral partitioning methods. Macrostate data clustering solves a variety of test cases that challenge other methods.

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Cluster Analysis Models, Statistical Models, Theoretical Normal Distribution Oligonucleotide Array Sequence Analysis Statistics as Topic Stochastic Processes Journal Article Research Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.

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Daniel Korenblum, David Shalloway. 2003-05-15. Macrostate data clustering.. https://doi.org/10.1103/physreve.67.056704

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