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论文类型：期刊论文

发表时间：2015-01-01

发表刊物：APPLIED SOFT COMPUTING

收录刊物：SCIE、EI

卷号：26

页面范围：21-30

ISSN号：1568-4946

关键字：Fuzzy clustering; Semantic interpretation; AFS algebra; Selection of simple concepts; Cluster validity index; Unsupervised learning

摘要：In the framework of Axiomatic Fuzzy Set (AFS) theory, we propose a new approach to data clustering. The objective of this clustering is to adhere to some principles of grouping exercised by humans when determining a structure in data. Compared with other clustering approaches, the proposed approach offers more detailed insight into the cluster's structure and the underlying decision making process. This contributes to the enhanced interpretability of the results via the representation capabilities of AFS theory. The effectiveness of the proposed approach is demonstrated by using real-world data, and the obtained results show that the performance of the clustering is comparable with other fuzzy rule-based clustering methods, and benchmark fuzzy clustering methods FCM and K-means. Experimental studies have shown that the proposed fuzzy clustering method can discover the clusters in the data and help specify them in terms of some comprehensive fuzzy rules. (C) 2014 Elsevier B.V. All rights reserved.