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论文类型：期刊论文
发表时间：2017-05-10
发表刊物：NEUROCOMPUTING
收录刊物：SCIE、EI、Scopus
卷号：237
页面范围：316-331
ISSN号：0925-2312
关键字：Incomplete data; Nearest neighbor interval; Kernel Fuzzy C-Means; Interval kernel distance
摘要：In the clustering of incomplete data, the processing of missing attribute values and the optimization procedure of clustering are always of concern. In this paper, a novel clustering method is proposed to cope with incomplete data. Owing to the uncertainty of missing values, we first estimate these values in the form of intervals using the nearest neighbor method, which utilizes information about the distribution of data and transforms incomplete data set into an interval-valued one. Then, a kernel method is introduced to increase the separability between data by implicitly mapping them into a higher dimensional feature space, in which a kernel-induced distance is used to replace the Euclidean distance so that the data can be processed in the original data space. We realize the kernel clustering of incomplete data set by means of a gradient-based alternating optimization of interval data clustering based on the interval kernel distance. Finally, the experimental results demonstrate that the proposed approach is superior in terms of its clustering performance.