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论文类型：期刊论文

发表时间：2010-10-01

发表刊物：EXPERT SYSTEMS WITH APPLICATIONS

收录刊物：SCIE、EI、Scopus

卷号：37

期号：10

页面范围：6942-6947

ISSN号：0957-4174

关键字：Clustering; Fuzzy c-means; Incomplete data; Nearest-neighbor intervals

摘要：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.