Application of cloud models in digital libraries

Hai Yan Kang*, Yan Fang Li, Shi Ping Tang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

In digital libraries, the traditional retrieval methods cannot efficiently retrieve uncertain concepts, and traditional evaluation methods have disadvantages on algorithm measures. To overcome above disadvantages, this paper proposes two new methods based on cloud models. One is to retrieve efficiently uncertain concepts, which can change the granularity of retrieval information. By this method it remarkably improves the recall and precision of information retrieval. Another is to evaluate efficiently retrieval algorithm, which can reflect not only average performance of an algorithm but also stability and randomicity. These two methods set up a transform of qualitative concepts and quantity. This kind of transform is carried out through strict mathematic means. Experimental data showed the method is practical. Results of evaluation will be more accurate and approach to the fact better.

Original languageEnglish
Title of host publicationProceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Pages3876-3879
Number of pages4
DOIs
Publication statusPublished - 2007
Event6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 - Hong Kong, China
Duration: 19 Aug 200722 Aug 2007

Publication series

NameProceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Volume7

Conference

Conference6th International Conference on Machine Learning and Cybernetics, ICMLC 2007
Country/TerritoryChina
CityHong Kong
Period19/08/0722/08/07

Keywords

  • Algorithm performance evaluation
  • Cloud model
  • Digital libraries(DLs)
  • Information retrieval(IR)
  • Uncertain concept

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