System of Intelligent Library Retrieval Based on Data Mining

Article Preview

Abstract:

Digital Library Retrieval involves the mathematical model of information retrieval algorithms, it is very important to design an algorithm to make the best books to extract the required information to involve the association rules and classification method for predicting the reader from the database and potentially fast and accurate information use problems. In this paper, the intelligent retrieval of books were analyzed using data mining algorithms can be studied books intelligent retrieval application, until practical algorithm to solve the retrieval model first developed fit the requirements and design a library information library books to achieve intelligent retrieval system.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1612-1615

Citation:

Online since:

June 2014

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] B. Berendt: Intelligent scientific authoring tools: Interactive data mining for constructive uses of citation networks, Information Processing & Management, Vol. 46 (2010), pp.1-10.

DOI: 10.1016/j.ipm.2009.08.002

Google Scholar

[2] N. G. Shaw: A comprehensive agent-based architecture for intelligent information retrieval in a distributed heterogeneous environment, Decision Support Systems, Vol. 32 (2002), pp.401-415.

DOI: 10.1016/s0167-9236(01)00128-2

Google Scholar

[3] J. Ropero, A. Gómez, A. Carrasco, C. León: A Fuzzy Logic intelligent agent for Information Extraction: Introducing a new Fuzzy Logic-based term weighting scheme, Expert Systems with Applications, Vol. 39 (2012), pp.4567-4581.

DOI: 10.1016/j.eswa.2011.10.009

Google Scholar

[4] J. Vega, J. E. Contributors: Intelligent methods for data retrieval in fusion databases, Fusion Engineering and Design, Vol. 83 (2008), pp.382-386.

DOI: 10.1016/j.fusengdes.2007.09.001

Google Scholar

[5] P. Srinivasan, M. E. Ruiz, D. H. Kraft: Vocabulary mining for information retrieval: rough sets and fuzzy sets, Information Processing & Management, Vol. 37 (2001), pp.15-38.

DOI: 10.1016/s0306-4573(00)00014-5

Google Scholar

[6] S. H. Liao, P. H. Chu, P. Y. Hsiao: Data mining techniques and applications – A decade review from 2000 to 2011, Expert Systems with Applications, Vol. 39 (2012), pp.11303-11311.

DOI: 10.1016/j.eswa.2012.02.063

Google Scholar

[7] C. Djellali: A New Digital Conceptual Model Oriented Corporate Memory Constructing: Taking Data Mining Models as a Case, Procedia Computer Science, Vol. 19 (2013), pp.977-983.

DOI: 10.1016/j.procs.2013.06.136

Google Scholar

[8] L. M. d. Carlantonio, B.A. Osiek, G. B. Xexéo: Intelligent Clustering Engine: A clustering gadget for Google Desktop, Expert Systems with Applications, Vol. 39 (2012), pp.9524-9533.

DOI: 10.1016/j.eswa.2012.02.101

Google Scholar