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论文类型：期刊论文

发表时间：2014-07-15

发表刊物：Journal of Computational Information Systems

收录刊物：EI

卷号：10

期号：14

页面范围：6007-6014

ISSN号：15539105

摘要：Data set with missing attributes is often encountered in practical applications. To solve the problem, missing attributes are represented as reconstructed intervals based on the nearest-neighbor rule in this paper. Furthermore, we propose a hybrid clustering algorithm for incomplete data set, ant colony guiding FCM based on missing attributes coding (AFA), which uses ant as a guider to search more accuracy imputations of missing attributes in the corresponding nearest-neighbor intervals. The experimental results for several UCI data sets show the superiority of the proposed global optimization algorithm. ? 2014 by Binary Information Press