Soft ranking in clustering

0Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.
Get full text

Abstract

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. © 2008 Elsevier B.V. All rights reserved.

Cite

CITATION STYLE

APA

Rovetta, S., Masulli, F., & Filippone, M. (2009). Soft ranking in clustering. Neurocomputing, 72(7–9), 2028–2031. https://doi.org/10.1016/j.neucom.2008.11.015

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free