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Soft Computing

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17.03.2015 | Methodologies and Application

Automatic constraints generation for semisupervised clustering: experiences with documents classification

verfasst von: Irene Diaz-Valenzuela, Vincenzo Loia, Maria J. Martin-Bautista, Sabrina Senatore, M. Amparo Vila

Erschienen in: Soft Computing | Ausgabe 6/2016

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Abstract

In the last times, semi-supervised clustering has been an area that has received a lot of attention. It is distinguished from more traditional unsupervised approaches on the use of a small amount of supervision to “steer” clustering. Unfortunately in the real world, the supervision is not always available: data to process are often too large and so the cost (in terms of time and human resources) for user-provided information is not conceivable. To address this issue, this work presents an automatic generation of the supervision, by the analysis of the data structure itself. This analysis is performed using a partitional clustering algorithm that discovers relationships between pairs of instances that may be used as a semi-supervision in the clustering process. The methodology has been studied in the document clustering domain, an area where novel approaches for accurate documents classifications are strongly required. Experimental result shows the validity of this approach.

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Titel: Automatic constraints generation for semisupervised clustering: experiences with documents classification
verfasst von: Irene Diaz-Valenzuela
Vincenzo Loia
Maria J. Martin-Bautista
Sabrina Senatore
M. Amparo Vila
Publikationsdatum: 17.03.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 6/2016
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-015-1643-3

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