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Title: Clustering and classifying text documents a revisit to tagging integration methods
Authors: Cunha,E
Álvaro Figueira
Issue Date: 2013
Abstract: In this paper we analyze and discuss two methods that are based on the traditional k-means for document clustering and that feature integration of social tags in the process. The first one allows the integration of tags directly into a Vector Space Model, and the second one proposes the integration of tags in order to select the initial seeds. We created a predictive model for the impact of the tags' integration in both models, and compared the two methods using the traditional k-means++ and the novel k-C algorithm. To compare the results, we propose a new internal measure, allowing the computation of the cluster compactness. The experimental results indicate that the careful selection of seeds on the k-C algorithm present better results to those obtained with the k-means++, with and without integration of tags.
metadata.dc.type: conferenceObject
Appears in Collections:CRACS - Articles in International Conferences

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