Title: Information retrieval on documents methodology based on entropy filtering methodologies

Authors: O. Lucia Quintero Montoya; Luisa F. Villa; Santiago Muñoz; Ana C. Ruiz Arenas; Manuela Bastidas

Addresses: Department of Mathematical Sciences, Mathematical Modelling Research Group, Universidad EAFIT, Carrera 49 No. 7, Sur – 50, Medellin, Colombia ' Department of Mathematical Sciences, Mathematical Modelling Research Group, Universidad EAFIT, Carrera 49 No. 7, Sur – 50, Medellin, Colombia ' Department of Mathematical Sciences, Mathematical Modelling Research Group, Universidad EAFIT, Carrera 49 No. 7, Sur – 50, Medellin, Colombia ' Department of Mathematical Sciences, Mathematical Modelling Research Group, Universidad EAFIT, Carrera 49 No. 7, Sur – 50, Medellin, Colombia ' Department of Mathematical Sciences, Mathematical Modelling Research Group, Universidad EAFIT, Carrera 49 No. 7, Sur – 50, Medellin, Colombia

Abstract: Information retrieval problem occurs when the target information is not available 'literally' into the set of documents. In problems in which the goal is to find 'hidden' information, it is important to develop hybrid methodologies or improve and design a new one. In this work the authors are dealing with identifying the most informative piece of data on a collection of documents, in order to obtain the best result on a posterior fuzzy clustering stage. The aim is to find similarities between the documents and a reference target, to establish relationships related to a non-literal feature. We propose to apply the well-known entropy term weighting scheme and then show a posterior different procedures to the right election of the interest data. This procedure brings the biggest amount of information within the smallest amount of data. Applying a specific selection procedure for a group of words, gives more information to differentiate and separate the documents after using the entropy weighting. This returns considerable results on the processing time and the right fuzzy clustering of the documents collection.

Keywords: text mining; entropy weighting; K-means clustering; fuzzy C-means clustering; information retrieval; entropy filtering; document collections.

DOI: 10.1504/IJBIDM.2015.071327

International Journal of Business Intelligence and Data Mining, 2015 Vol.10 No.3, pp.280 - 296

Received: 08 Apr 2015
Accepted: 11 Apr 2015

Published online: 20 Aug 2015 *

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