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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Interactive textual feature selection for consensus clustering

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Correa, Geraldo N. [1] ; Marcacini, Ricardo M. [2] ; Hruschka, Eduardo R. [1] ; Rezende, Solange O. [1]
Total Authors: 4
[1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil
[2] Univ Fed Mato Grosso do Sul, CPTL, Tres Lagoas, MS - Brazil
Total Affiliations: 2
Document type: Journal article
Source: PATTERN RECOGNITION LETTERS; v. 52, p. 25-31, JAN 15 2015.
Web of Science Citations: 4

Consensus clustering and interactive feature selection are very useful methods to extract and manage knowledge from texts. While consensus clustering allows the aggregation of different clustering solutions into a single robust clustering solution, the interactive feature selection facilitates the incorporation of the users' experience in the clustering tasks by selecting a set of textual features, i.e., including user's supervision at the term-level. We propose an approach for incorporating interactive textual feature selection into consensus clustering. Experimental results on several text collections demonstrate that our approach significantly improves consensus clustering accuracy, even when only few textual features are selected by the users. (C) 2014 Elsevier By. All rights reserved, (AU)

FAPESP's process: 14/08996-0 - Machine learning for WebSensors: algorithms and applications
Grantee:Solange Oliveira Rezende
Support type: Regular Research Grants