A Multicriteria Decision Making Approach for Estimating the Number of Clusters in a Data Set

Determining the number of clusters in a data set is an essential yet difficult step in cluster analysis. Since this task involves more than one criterion, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper proposes a multiple criteria decision making (MCDM)-based app...

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Vydáno v:PloS one Ročník 7; číslo 7; s. e41713
Hlavní autoři: Peng, Yi, Zhang, Yong, Kou, Gang, Shi, Yong
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 27.07.2012
Public Library of Science (PLoS)
Témata:
Algorithms
Analysis
Artificial Intelligence
Breast cancer
Cluster Analysis
Clustering
Clusters
Computer Science
Data mining
Datasets
Decision Making
Engineering
Estimation
Forensic sciences
Information technology
Knowledge discovery
Learning algorithms
Linear programming
Localization
Machine learning
Mathematics
Methods
Multiple criterion
Social and Behavioral Sciences
Studies
Validity
China
ISSN:1932-6203, 1932-6203
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Shrnutí:Determining the number of clusters in a data set is an essential yet difficult step in cluster analysis. Since this task involves more than one criterion, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper proposes a multiple criteria decision making (MCDM)-based approach to estimate the number of clusters for a given data set. In this approach, MCDM methods consider different numbers of clusters as alternatives and the outputs of any clustering algorithm on validity measures as criteria. The proposed method is examined by an experimental study using three MCDM methods, the well-known clustering algorithm--k-means, ten relative measures, and fifteen public-domain UCI machine learning data sets. The results show that MCDM methods work fairly well in estimating the number of clusters in the data and outperform the ten relative measures considered in the study.
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Conceived and designed the experiments: YP GK YZ YS. Performed the experiments: YZ YP. Analyzed the data: YP YZ. Wrote the paper: YP GK.
Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0041713