Efficient Information Retrieval using Fuzzy Self Construction Algorithm
|
10.5120/18167-9031 |
S.niveditha, T.malathi and S.r.sivaranjhani. Article: Efficient Information Retrieval using Fuzzy Self Construction Algorithm. International Journal of Computer Applications 104(1):18-20, October 2014. Full text available. BibTeX
@article{key:article,
author = {S.niveditha and T.malathi and S.r.sivaranjhani},
title = {Article: Efficient Information Retrieval using Fuzzy Self Construction Algorithm},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {104},
number = {1},
pages = {18-20},
month = {October},
note = {Full text available}
}
Abstract
Different users have different search goals when they submit a query to a search engine. In this paper we aim at discovering the number of diverse user's search goal for giving a query and for each goal a keyword is associated automatically. We initially derive user's search goal for a query by clustering our proposed feedback conclave. Then the feedback conclave is mapped to pseudo-documents so that the user's needs are retrieved efficiently. Finally, these pseudo documents are then clustered to deduce user search goals and depict them with some keywords. Though K means clustering is used in the existing system sometimes queries may not exactly represent user specific information needs. This method only finds whether a pair of query is belonging to the same set of goal and does not look into goal in detail. Hence we put forward a fuzzy similarity-based self-constructing algorithm for feature clustering. Our method works efficiently and will return provide better inferred properties than any other method.
References
- Zheng Lu, HongyuanZha, Xiaokang Yang, Weiyao Lin and ZhaohuiZheng, "A New Algorithm for Inferring User Search Goals with Feedback Sessions", IEEE Transactions on knowledge and data engineering, VOL. 25, NO. 3, pp 502-513, March 2013.
- James C. Bezdek, Robert Ehrlich, William Full, "FCM- The Fuzzy c- Means Clustering Algorithm", Computers and Geosciences, VOL. 10, NO. 2-3, pp 191-203, 1984.
- D. Beeferman and A. Berger, "Agglomerative Clustering of aSearch Engine Query Log," Proc. Sixth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (SIGKDD '00), pp. 407-416,2000.
- S. Beitzel, E. Jensen, A. Chowdhury, and O. Frieder, "VaryingApproaches to Topical Web Query Classification," Proc. 30th Ann. Int'l ACM SIGIR