点击次数：

论文类型：期刊论文

发表时间：2016-05-01

发表刊物：KNOWLEDGE-BASED SYSTEMS

收录刊物：SCIE、EI、Scopus

卷号：99

页面范围：51-70

ISSN号：0950-7051

关键字：Fuzzy clustering; Incomplete data; Missing value; Probabilistic information granules; Alternating optimization

摘要：Missing values are a common phenomenon when dealing with real-world data sets. Analysis of incomplete data sets has become an active area of research. In this paper, we focus on the problem of clustering incomplete data, which is intended to introduce some prior distribution information of the missing values into the algorithm of fuzzy clustering. First, non-parametric hypothesis testing is employed to describe the missing values adhering to a certain Gaussian distribution as probabilistic information granules based on the nearest neighbors of incomplete data. Second, we propose a novel clustering model, in which probabilistic information granules of missing values are incorporated into the Fuzzy C-Means clustering of incomplete data by involving the maximum likelihood criterion. Third, the clustering model is optimized by using a tri-level alternating optimization utilizing the method of Lagrange multipliers. The convergence and the time complexity of the clustering algorithm are also discussed. The experiments reported both on synthetic and real-world data sets demonstrate that the proposed approach can effectively realize clustering of incomplete data. (C) 2016 Elsevier B.V. All rights reserved.