Multicriteria Decision Making Approach for Cluster Validation | Zendy

Yi Peng | Zendy; Yong Zhang | Zendy; Gang Kou | Zendy; Jun Li | Zendy; Yong Shi | Zendy

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Multicriteria Decision Making Approach for Cluster Validation

Author(s) -

Yi Peng,

Yong Zhang,

Gang Kou,

Jun Li,

Yong Shi

Publication year - 2012

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2012.04.140

Subject(s) - computer science , cluster (spacecraft) , data mining , operations research , data science , programming language , engineering

This paper proposes a multiple criteria decision making (MCDM)-based framework to address two fundamental issues in cluster validation: 1) evaluation of clustering algorithms and 2) estimation of the optimal cluster number for a given data set. Since both issues involve more than one criterion, they can be modeled as multiple criteria decision making (MCDM) problems. The proposed framework is examined by an experimental study. The results suggest that MCDM methods are practical tools for the evaluation of clustering algorithms. In addition, the selected MCDM method, PROMETHEE II can estimate the optimal numbers of clusters for ten out of fifteen datasets by adjusting the weights of criteria

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