文章基本信息

标题：Automated Information Retrieval Model Using FP Growth Based Fuzzy Particle Swarm Optimization
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作者：Raja Varma Pamba ; Elizabeth Sherly ; Kiran Mohan 等
期刊名称：International Journal of Computer Science & Information Technology (IJCSIT)
印刷版ISSN：0975-4660
电子版ISSN：0975-3826
出版年度：2017
卷号：9
期号：1
页码：105
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：To mine out relevant facts at the time of need from web has been a tenuous task. Research on diverse fieldsare fine tuning methodologies toward these goals that extracts the best of information relevant to the userssearch query. In the proposed methodology discussed in this paper find ways to ease the search complexitytackling the severe issues hindering the performance of traditional approaches in use. The proposedmethodology find effective means to find all possible semantic relatable frequent sets with FP Growthalgorithm. The outcome of which is the further source of fuel for Bio inspired Fuzzy PSO to find the optimalattractive points for the web documents to get clustered meeting the requirement of the search querywithout losing the relevance. On the whole the proposed system optimizes the objective function ofminimizing the intra cluster differences and maximizes the inter cluster distances along with retention of allpossible relationships with the search context intact. The major contribution being the system finds allpossible combinations matching the user search transaction and thereby making the system moremeaningful. These relatable sets form the set of particles for Fuzzy Clustering as well as PSO and thusbeing unbiased and maintains a innate behaviour for any number of new additions to follow the herdbehaviour’s evaluations reveals the proposed methodology fares well as an optimized and effectiveenhancements over the conventional approaches.
关键词：Information retrieval; Clustering; Fuzzy particle swarm optimization & Frequent Pattern growth