Probabilistic validation approach for clustering

M. Har-even; V. L. Brailovsky

doi:10.1016/0167-8655(95)00073-P

Probabilistic validation approach for clustering

M. Har-even
, V. L. Brailovsky^*

^*Corresponding author for this work

School of Computer Science and AI

Tel Aviv University

Research output: Contribution to journal › Article › peer-review

22 Scopus citations

Abstract

The suggested approach combines the phases of cluster validity and cluster tendency inside the scope of the clustering algorithm. The algorithm is based on a probabilistic approach and is invariant to the scaling of features. The result is an efficient algorithm whose performance is demonstrated on real and synthetic data.

Original language	English
Pages (from-to)	1189-1196
Number of pages	8
Journal	Pattern Recognition Letters
Volume	16
Issue number	11
DOIs	https://doi.org/10.1016/0167-8655(95)00073-P
State	Published - Nov 1995

Keywords

Cluster analysis
Probabilistic validation
Projection pursuit
Simulating annealing
Unsupervised hierarchical clustering

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10.1016/0167-8655(95)00073-P

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title = "Probabilistic validation approach for clustering",

abstract = "The suggested approach combines the phases of cluster validity and cluster tendency inside the scope of the clustering algorithm. The algorithm is based on a probabilistic approach and is invariant to the scaling of features. The result is an efficient algorithm whose performance is demonstrated on real and synthetic data.",

keywords = "Cluster analysis, Probabilistic validation, Projection pursuit, Simulating annealing, Unsupervised hierarchical clustering",

author = "M. Har-even and Brailovsky, \{V. L.\}",

year = "1995",

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AU - Brailovsky, V. L.

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N2 - The suggested approach combines the phases of cluster validity and cluster tendency inside the scope of the clustering algorithm. The algorithm is based on a probabilistic approach and is invariant to the scaling of features. The result is an efficient algorithm whose performance is demonstrated on real and synthetic data.

AB - The suggested approach combines the phases of cluster validity and cluster tendency inside the scope of the clustering algorithm. The algorithm is based on a probabilistic approach and is invariant to the scaling of features. The result is an efficient algorithm whose performance is demonstrated on real and synthetic data.

KW - Cluster analysis

KW - Probabilistic validation

KW - Projection pursuit

KW - Simulating annealing

KW - Unsupervised hierarchical clustering

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JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

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Probabilistic validation approach for clustering

Abstract

Keywords

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