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A Tolerance Rough Set Based Overlapping Clustering for the DBLP Data

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5 Author(s)
Obadi, G. ; Dept. of Comput. Sci., VSB-Tech. Univ. of Ostrava, Ostrava, Czech Republic ; Dráždilová, P. ; Hlaváček, L. ; Martinovic, J.
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In the article there is presented comparison of overlapping clustering methods for data mining of DBLP datasets. For the analysis, the DBLP data sets were pre-processed, while each journal has been assigned attributes, defined by its topics. The data collection can be described as vague and uncertain; obtained clusters and applied queries do not necessarily have crisp boundaries. The authors presented clustering through a tolerance rough set method (TRSM) and fuzzy c-mean (FCM) algorithm for journal recommendation based on topic search. The comparison of both clustering methods was presented using different measures of similarity.

Published in:

Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on  (Volume:3 )

Date of Conference:

Aug. 31 2010-Sept. 3 2010