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Silhouette + attraction: A simple and effective method for text clustering†

Published online by Cambridge University Press:  14 August 2015

MARCELO L. ERRECALDE ,
LETICIA C. CAGNINA  and
PAOLO ROSSO
MARCELO L. ERRECALDE
Affiliation:
LIDIC, Universidad Nacional de San Luis, San Luis, Argentina e-mail: merreca@unsl.edu.ar, lcagnina@gmail.com
LETICIA C. CAGNINA
Affiliation:
LIDIC, Universidad Nacional de San Luis, San Luis, Argentina e-mail: merreca@unsl.edu.ar, lcagnina@gmail.com
PAOLO ROSSO
Affiliation:
NLE Lab, PRHLT Research Center, Universitat Politècnica de València, Valencia, España e-mail: prosso@dsic.upv.es
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Abstract

This article presents silhouette–attraction (Sil–Att), a simple and effective method for text clustering, which is based on two main concepts: the silhouette coefficient and the idea of attraction. The combination of both principles allows us to obtain a general technique that can be used either as a boosting method, which improves results of other clustering algorithms, or as an independent clustering algorithm. The experimental work shows that Sil–Att is able to obtain high-quality results on text corpora with very different characteristics. Furthermore, its stable performance on all the considered corpora is indicative that it is a very robust method. This is a very interesting positive aspect of Sil–Att with respect to the other algorithms used in the experiments, whose performances heavily depend on specific characteristics of the corpora being considered.

Type
Articles
Information
Natural Language Engineering , Volume 22 , Issue 5 , September 2016 , pp. 687 - 726
Copyright
Copyright © Cambridge University Press 2015 

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Footnotes

†

This research work has been partially funded by UNSL, CONICET (Argentina), DIANA-APPLICATIONS-Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) research project, and the WIQ-EI IRSES project (grant no. 269180) within the FP 7 Marie Curie People Framework on Web Information Quality Evaluation Initiative. The work of the third author was done also in the framework of the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.

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