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Bees swarm optimization guided by data mining techniques for document information retrieval

Author(s)
Djenouri, YoucefBelhadi, AsmaBelkebir, Riadh
Issued Date
2018-03
DOI
10.1016/j.eswa.2017.10.042
URI
https://scholarworks.unist.ac.kr/handle/201301/23214
Fulltext
http://www.sciencedirect.com/science/article/pii/S0957417417307248?via%3Dihub
Citation
EXPERT SYSTEMS WITH APPLICATIONS, v.94, pp.126 - 136
Abstract
This paper explores advances in the data mining field to solve the fundamental Document Information Retrieval problem. In the proposed approach, useful knowledge is first discovered by using data mining techniques, then swarms use this knowledge to explore the whole space of documents intelligently. We have investigated two data mining techniques in the preprocessing step. The first one aims to split the collection of documents into similar clusters by using the k-means algorithm, while the second one extracts the most closed frequent terms on each cluster already created using the DCI_Closed algorithm. For the solving step, BSO (Bees Swarm Optimization) is used to explore the cluster of documents deeply. The proposed approach has been evaluated on well-known collections such as CACM (Collection of ACM), TREC (Text REtrieval Conference), Webdocs, and Wikilinks, and it has been compared to state-of-the-art data mining, bio-inspired and other documents information retrieval based approaches. The results show that the proposed approach improves the quality of returned documents considerably, with a competitive computational time compared to state-of-the-art approaches.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
ISSN
0957-4174
Keyword (Author)
Information retrievalData miningBig dataBSO algorithmBio-inspired methods
Keyword
K-MEANSALGORITHMITEMSETSRANKINGMODELS

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