Name
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. |
Year | DOI | Venue |
|---|---|---|
1993 | 10.1162/neco.1993.5.1.89 | Neural Computation |
Keywords | Field | DocType |
new concept intoclustering,clustering data,algorithm thatincorporates scale,cluster density,algorithm work,synthetic aperture radar image,cluster independence,new clustering algorithm,cluster size,scale space,cluster center,fixed point,synthetic aperture radar,rating scale,statistical mechanics,free energy,local minima,information theory,thermodynamics | k-medians clustering,Canopy clustering algorithm,Fuzzy clustering,CURE data clustering algorithm,Clustering high-dimensional data,Mathematical optimization,Correlation clustering,Algorithm,Artificial intelligence,Cluster analysis,Mathematics,Single-linkage clustering | Journal |
Volume | Issue | ISSN |
5 | 1 | 0899-7667 |
Citations | PageRank | References |
34 | 13.12 | 6 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiu-fai Isaac Wong | 1 | 62 | 16.77 |