Clustering data by melting
- Creators
- Wong, Yiu-fai
Abstract
We derive a new clustering algorithm based on information theory and statistical mechanics, which is the only algorithm that incorporates scale. It also introduces a new concept into clustering: cluster independence. The cluster centers correspond to the local minima of a thermodynamic free energy, which are identified as the fixed points of a one-parameter nonlinear map. The algorithm works by melting the system to produce a tree of clusters in the scale space. Melting is also insensitive to variability in cluster densities, cluster sizes, and ellipsoidal shapes and orientations. We tested the algorithm successfully on both simulated data and a Synthetic Aperture Radar image of an agricultural site with 12 attributes for crop identification.
Additional Information
© 1993 MIT Press. Received 16 January 1992; accepted 12 May 1992. I am deeply indebted to my thesis advisor Edward C. Posner for his patience, encouragement, and advice. I benefited much from him. I thank Professor Steve Wiggins at Caltech for clarifying some concepts on the local bifurcations. This work is supported by Pacific Bell through a grant to the California Institute of Technology and by NASA through the Caltech Jet Propulsion Laboratory, as well as a Charles Lee Powell Foundation Graduate Fellowship at Caltech.Files
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Additional details
- Eprint ID
- 13606
- Resolver ID
- CaltechAUTHORS:WONnc93
- California Institute of Technology
- NASA
- Pacific Bell
- Created
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2009-05-11Created from EPrint's datestamp field
- Updated
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2021-11-08Created from EPrint's last_modified field