article

Probabilistic validation approach for clustering

Authors:

M. Har-even,

V. L. BrailovskyAuthors Info & Claims

Pattern Recognition Letters, Volume 16, Issue 11

Pages 1189 - 1196

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

Published: 01 November 1995 Publication History

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.

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Cited By

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Wang JZhao D(2023)Social Vulnerability Assessment Based on Projection Pursuit Dynamic Cluster ModelProceedings of the 2023 6th International Conference on Information Management and Management Science10.1145/3625469.3625510(176-180)Online publication date: 25-Aug-2023
https://dl.acm.org/doi/10.1145/3625469.3625510
Figuera PCuzzocrea AGarcía Bringas P(2023)Probability Density Function for Clustering ValidationHybrid Artificial Intelligent Systems10.1007/978-3-031-40725-3_12(133-144)Online publication date: 5-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-40725-3_12
Fred AJain A(2005)Combining Multiple Clusterings Using Evidence AccumulationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2005.11327:6(835-850)Online publication date: 1-Jun-2005
https://dl.acm.org/doi/10.1109/TPAMI.2005.113
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Index Terms

Probabilistic validation approach for clustering
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Theory of computation
  1. Models of computation
    1. Probabilistic computation

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Information

Published In

Pattern Recognition Letters Volume 16, Issue 11

Nov. 1995

103 pages

ISSN:0167-8655

Editors:
E. Backer
Delft Univ. of Technology, Delft, Netherlands
,
E. S. Gelsema
Erasmus Univ., Rotterdam, Netherlands

Issue’s Table of Contents

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 November 1995

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Wang JZhao D(2023)Social Vulnerability Assessment Based on Projection Pursuit Dynamic Cluster ModelProceedings of the 2023 6th International Conference on Information Management and Management Science10.1145/3625469.3625510(176-180)Online publication date: 25-Aug-2023
https://dl.acm.org/doi/10.1145/3625469.3625510
Figuera PCuzzocrea AGarcía Bringas P(2023)Probability Density Function for Clustering ValidationHybrid Artificial Intelligent Systems10.1007/978-3-031-40725-3_12(133-144)Online publication date: 5-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-40725-3_12
Fred AJain A(2005)Combining Multiple Clusterings Using Evidence AccumulationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2005.11327:6(835-850)Online publication date: 1-Jun-2005
https://dl.acm.org/doi/10.1109/TPAMI.2005.113
Cheung Y(2005)On Rival Penalization Controlled Competitive Learning for Clustering with Automatic Cluster Number SelectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2005.18417:11(1583-1588)Online publication date: 1-Nov-2005
https://dl.acm.org/doi/10.1109/TKDE.2005.184
Cheung Y(2003)k-meansPattern Recognition Letters10.1016/S0167-8655(03)00146-624:15(2883-2893)Online publication date: 1-Nov-2003
https://dl.acm.org/doi/10.1016/S0167-8655%2803%2900146-6
Fred AJain A(2002)Evidence Accumulation Clustering Based on the K-Means AlgorithmProceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition10.5555/645890.671406(442-451)Online publication date: 6-Aug-2002
https://dl.acm.org/doi/10.5555/645890.671406

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Abstract

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Cited By

Index Terms

Recommendations

Far efficient K-means clustering algorithm

Evolutionary rough k-medoid clustering

Proficient Normalised Fuzzy K-Means With Initial Centroids Methodology

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