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An Experimental Study on Selecting Association Terms Using Text Mining Techniques
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Abstract
In this study, experiments for selection of association terms were conducted in order to discover the optimum method in selecting additional terms that are related to an initial query term. Association term sets were generated by using support, confidence, and lift measures of the Apriori algorithm, and also by using the similarity measures such as GSS, Jaccard coefficient, cosine coefficient, and Sokal & Sneath 5, and mutual information. In performance evaluation of term selection methods, precision of association terms as well as the overlap ratio of association terms and relevant documents' indexing terms were used. It was found that Apriori algorithm and GSS achieved the highest level of performances.
- keywords
- text mining, association terms, similarity measures, Apriori algorithm, term clustering, 텍스트 마이닝, 연관용어, 유사계수, Apriori 알고리즘, 용어 클러스터링
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