Using fuzzy techniques for subspace clustering, our algorithm avoids the difficulty of choosing appropriate cluster dimensions for each cluster during the ...
Abstract. In fuzzy clustering algorithms each object has a fuzzy mem- bership associated with each cluster indicating the degree of association.
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Using fuzzy techniques for subspace clustering, our algorithm avoids the difficulty of choosing appropriate cluster dimensions for each cluster during the ...
Subspace clus- tering algorithms localize the search for relevant dimensions allowing them to find clusters that exist in multiple, possi- bly overlapping ...
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A fuzzy subspace algorithm for clustering high dimensional data. R. Hathaway et al. An improved convergence theorem for the fuzzy c-means clustering algorithms.
In this survey, we try to clarify: (i) the different problem definitions related to subspace clustering in general; (ii) the specific difficulties encountered ...
We introduce a soft subspace clustering algorithm, a Self-organizing Map (SOM) with a time-varying structure, to cluster data without any prior knowledge.
We use this approach for developing the CBK-Modes (Correlation-based K-modes); a soft subspace clustering algorithm that extends the basic k-modes by using the ...
Abstract: A genetic algorithm-based high-dimensional data clustering technique, called GA-HDclustering, is proposed in this paper.
The objective of the proposed model is to improve the performance of traditional H-K clustering and overcome the limitations such as high computational ...