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论文类型：期刊论文

发表时间：2015-03-15

发表刊物：Journal of Computational Information Systems

收录刊物：EI、Scopus

卷号：11

期号：6

页面范围：2139-2146

ISSN号：15539105

摘要：Missing data handling is a challenging issue often dealt with in data mining and pattern classification. In this paper, a fuzzy c-means clustering algorithm based on pseudo-nearest-neighbor intervals for incomplete data is given. The data are first completed using the pseudo-nearest-neighbor intervals approach, then the data set can be clustered based on the fuzzy c-means algorithm for interval-valued data. The proposed algorithm estimates the missing attribute values without normalization, thus captures the essence of pattern similarities in the original untouched data set. Additionally, the pseudo-nearest-neighbor intervals representation takes account of implicit uncertainly of missing attribute values, and considers the angle between incomplete data and other data as well. Results on several incomplete data sets demonstrate the effectiveness of the proposed algorithm. Copyright ? 2015 Binary Information Press.