a multicriteria decision making approach for estimating the number of clusters in a data set多准则决策方法评估集群的数量在一个数据集.pdfVIP
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a multicriteria decision making approach for estimating the number of clusters in a data set多准则决策方法评估集群的数量在一个数据集
A Multicriteria Decision Making Approach for Estimating
the Number of Clusters in a Data Set
1 1 1 2,3
Yi Peng , Yong Zhang , Gang Kou *, Yong Shi
1 School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, Sichuan, China, 2 CAS Research Center on Fictitious
Economy and Data Sciences, Beijing, China, 3 College of Information Science Technology, University of Nebraska at Omaha, Omaha, Nebraska, United States of America
Abstract
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.
Citation: Peng Y, Zhang Y, Kou G, Shi Y (2012) A Multicriteria Decision Making Approach for Estimating the Number of Clusters in a Data Set. PLoS ONE 7(7):
e41713. doi:10.1371/journal.pone.0041713
Editor: Frank Emmert-Streib, Queen’s University Belfast, United Kingdom
Received April 1, 2012; Accepted June 27, 2012; Published July 27, 2012
Copyright: 2012 Peng et al. This is an ope
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