Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Leaders-subleaders: an efficient hierarchical clustering algorithm for large data sets
Pattern Recognition Letters
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Rough-fuzzy weighted k-nearest leader classifier for large data sets
Pattern Recognition
Density-weighted fuzzy c-means clustering
IEEE Transactions on Fuzzy Systems
A Novel Possibilistic Fuzzy Leader Clustering Algorithm
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Hyperspherical possibilistic fuzzy c-means for high-dimensional data clustering
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
A robust fuzzy local information C-means clustering algorithm
IEEE Transactions on Image Processing
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
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
International Journal of Hybrid Intelligent Systems
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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 λ-cut set is defined to produce the overlapping clusters autonomously. The comparative experiments with synthetic and standard data sets show that the proposed algorithm is valid, efficient, and has better accuracy. The experiments with the web users access paths data set show that the proposed algorithm is capable of clustering access paths at an acceptable computational expense.