Macrostate Data Clustering

Details Macrostate Data Clustering Macrostate Data Clustering

2003-06-19

arxiv.org/abs/physics/0306145v1

Phys. Rev. E 67, 056704 (2003) (12 pages)

Physics

Data Analysis, Statistics and Probability

keywords: cluster analysis, clustering, pattern recognition, spectral graph theory, dynamic eigenvectors, machine learning, ma

Scientific paper

10.1103/PhysRevE.67.056704

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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