A Semantic approach for Text Clustering using WordNet based on Multi-Objective Genetic Algorithms

HAN HEE, HAHM and JONG JOO, LEE and JUNG SONG, LEE and SOON CHEOL, PARK (2015) A Semantic approach for Text Clustering using WordNet based on Multi-Objective Genetic Algorithms. In: Third International Conference on Advances in Computing, Electronics and Communication - ACEC 2015, 10-11 October, 2015, Zurich, Switzerland.

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Abstract

In this paper, we propose a method of Multi- Objective Genetic Algorithms (MOGAs), NSGA-II and SPEA2, for document clustering with semantic similarity measures based on WordNet. The MOGAs showed a high performance compared to other clustering algorithms. The main problem in the application of MOGAs for document clustering in the Vector Space Model (VSM) is that it ignores relationships between important terms or words. The hierarchical structure of WordNet as thesaurus-based ontology is an effective technique, which is used in semantic similarity measure. We tested these algorithms on Reuter-21578 collection data sets and compared them with Genetic Algorithms (GA) in conjunction with the semantic similarity measures based on WordNet. Also, we used F-measure to evaluate the performance of these clustering algorithms. The experimental results show that the performance of MOGAs based on WordNet is superior to those of the other clustering algorithms in the same similarity environments.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Document Clustering, Multi-Objective Genetic Algorithm, Semantic Similarity Measure, WordNet
Depositing User: Mr. John Steve
Date Deposited: 19 Apr 2019 12:08
Last Modified: 19 Apr 2019 12:08
URI: http://publications.theired.org/id/eprint/1410

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