Soft Rank Clustering
Clustering methods provide an useful tool to tackle the problem of exploring large-dimensional data. However many common approaches suffer from being applied in high-dimensional spaces. Building on a dissimilarity-based representation of data, we propose a dimensionality reduction technique which preserves the clustering structure of the data. The technique is designed for cases in which data dimensionality is large compared to the number of available observations. In these cases, we represent data in the space of soft D-ranks, by applying the concept of fuzzy ranking. A clustering procedure is then applied. Experimental results show that the method is able to retain the necessary information, while considerably reducing dimensionality.
KeywordsCluster Algorithm Linkage Method Dissimilarity Matrix Dimensionality Reduction Technique Neighbor Linkage
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