Probabilistic D-clustering

Date

2008-06-01

Author

Ben-Israel, Adi
İyigün, Cem

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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

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Citation Formats

A. Ben-Israel and C. İyigün, “Probabilistic D-clustering,” JOURNAL OF CLASSIFICATION, pp. 5–26, 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47248.