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Indexed by:期刊论文

Date of Publication:2010-10-01

Journal:EXPERT SYSTEMS WITH APPLICATIONS

Included Journals:SCIE、EI、Scopus

Volume:37

Issue:10

Page Number:6942-6947

ISSN No.:0957-4174

Key Words:Clustering; Fuzzy c-means; Incomplete data; Nearest-neighbor intervals

Abstract:Partially missing data sets are a prevailing problem in clustering analysis. In this paper, missing attributes are represented as intervals, and a novel fuzzy c-means algorithm for incomplete data based on nearest-neighbor intervals is proposed. The algorithm estimates the nearest-neighbor interval representation of missing attributes by using the attribute distribution information of the data sets sufficiently, which can enhances the robustness of missing attribute imputation compared with other numerical imputation methods. Also, the convex hyper-polyhedrons formed by interval prototypes can present the uncertainty of missing attributes, and simultaneously reflect the shape of the clusters to some degree, which is helpful in enhancing the robustness of clustering analysis. Comparisons and analysis of the experimental results for several UCI data sets demonstrate the capability of the proposed algorithm. (C) 2010 Elsevier Ltd. All rights reserved.