Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Comments on “A possibilistic approach to clustering”
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
Autonomous knowledge-oriented clustering using decision-theoretic rough set theory
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Web users access paths clustering based on possibilistic and fuzzy sets theory
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
A novel possibilistic fuzzy leader clustering algorithm
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
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The clusters tend to have vague or imprecise boundaries in some fields such as web mining, since clustering has been widely used. Fuzzy clustering is sensitive to noises and possibilistic clustering is sensitive to the initialization of cluster centers and generates coincident clusters. Based on combination of fuzzy clustering and possibilistic clustering, a novel possibilistic fuzzy leader (PFL) clustering algorithm is proposed in this paper to overcome these shortcomings. Considering the advantages of the leader algorithm in time efficiency and the initialization of cluster, the framework of the leader algorithm is used. In addition, a 驴-cut set is defined to process the overlapping clusters adaptively. The comparison of experimental results shows that our proposed algorithm is valid, efficient, and has better accuracy.