Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Characterization and detection of noise in clustering
Pattern Recognition Letters
A continuous metric scaling solution for a random variable
Journal of Multivariate Analysis
On relational data versions of c-means algorithms
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
A robust algorithm for automatic extraction of an unknown number of clusters from noisy data
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
Comments on “A possibilistic approach to clustering”
IEEE Transactions on Fuzzy Systems
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
ECM: An evidential version of the fuzzy c-means algorithm
Pattern Recognition
Dealing with non-metric dissimilarities in fuzzy central clustering algorithms
International Journal of Approximate Reasoning
Fuzzy relational clustering around medoids: A unified view
Fuzzy Sets and Systems
Information Sciences: an International Journal
Clustering with proximity knowledge and relational knowledge
Pattern Recognition
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This paper initially describes the relational counterpart of possibilistic c-means (PCM) algorithm, called relational PCM (or RPCM). RPCM is then improved to better handle arbitrary dissimilarity data. First, a re-scaling of the PCM membership function is proposed in order to obtain zero membership values when the distance to prototype equals the maximum value allowed in bounded dissimilarity measures. Second, a heuristic method of reference distance initialisation is provided which diminishes the known PCM tendency of producing coincident clusters. Finally, RPCM improved with our initialisation strategy is tested on both synthetic and real data sets with satisfactory results.