Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
ACM Computing Surveys (CSUR)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Machine Learning
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Approximate clustering in very large relational data: Research Articles
International Journal of Intelligent Systems
A survey of kernel and spectral methods for clustering
Pattern Recognition
Selective sampling for approximate clustering of very large data sets
International Journal of Intelligent Systems
Dual fuzzy-possibilistic coclustering for categorization of documents
IEEE Transactions on Fuzzy Systems
Low-complexity fuzzy relational clustering algorithms for Web mining
IEEE Transactions on Fuzzy Systems
Robust fuzzy clustering of relational data
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
Linear Fuzzy Clustering With Selection of Variables Using Graded Possibilistic Approach
IEEE Transactions on Fuzzy Systems
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Locality sensitive C-means clustering algorithms
Neurocomputing
Fuzzy relational clustering around medoids: A unified view
Fuzzy Sets and Systems
Clustering with proximity knowledge and relational knowledge
Pattern Recognition
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
LinkFCM: Relation integrated fuzzy c-means
Pattern Recognition
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The well known k-medoids clustering approach groups objects through finding k representative objects based on the pairwise (dis)similarities of objects in the data set. In real applications, using only one object to capture or interpret each cluster may not be sufficient enough which in turn could affect the accuracy of the data analysis. In this paper, we propose a new fuzzy clustering approach called PFC for (dis)similarity-based data or relational data analysis. In PFC, objects in each fuzzy cluster carry various weights called prototype weights to represent their degrees of representativeness in that cluster. This mechanism enables each cluster to be represented by more than one objects. Compared with existing clustering approaches for relational data, PFC is able to capture the underlying structures of the data more accurately and provide richer information for the description of the resulting clusters. We introduce the detailed formulation of PFC and provide the analytical as well as experimental studies to demonstrate the merits of the proposed approach.