Reasonable properties for the ordering of fuzzy quantities (I)
Fuzzy Sets and Systems
Reasonable properties for the ordering of fuzzy quantities (II)
Fuzzy Sets and Systems
Redefining Clustering for High-Dimensional Applications
IEEE Transactions on Knowledge and Data Engineering
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Vector quantization and fuzzy ranks for image reconstruction
Image and Vision Computing
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Due to the diffusion of large-dimensional data sets (e.g., in DNA microarray or document organization and retrieval applications), there is a growing interest in clustering methods based on a proximity matrix. These have the advantage of being based on a data structure whose size only depends on cardinality, not dimensionality. In this paper, we propose a clustering technique based on fuzzy ranks. The use of ranks helps to overcome several issues of large-dimensional data sets, whereas the fuzzy formulation is useful in encoding the information contained in the smallest entries of the proximity matrix. Comparative experiments are presented, using several standard hierarchical clustering techniques as a reference.