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Probabilistic D-clustering
Date
2008-06-01
Author
Ben-Israel, Adi
İyigün, Cem
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
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We present a new iterative method for probabilistic clustering of data. Given clusters, their centers and the distances of data points from these centers, the probability of cluster membership at any point is assumed inversely proportional to the distance from (the center of) the cluster in question. This assumption is our working principle.
Subject Keywords
Lustering
,
Probabilistic clustering
,
Mahalanobis distance
,
Harmonic mean
,
Joint distance function
,
Weiszfeld method
,
Similarity matrix
URI
https://hdl.handle.net/11511/47248
Journal
JOURNAL OF CLASSIFICATION
DOI
https://doi.org/10.1007/s00357-008-9002-z
Collections
Department of Industrial Engineering, Article