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dc.contributor.authorZhang, Genxiangzh_CN
dc.contributor.authorChen, Haishanzh_CN
dc.contributor.author陈海山zh_CN
dc.date.accessioned2015-07-22T02:16:07Z
dc.date.available2015-07-22T02:16:07Z
dc.date.issued2009zh_CN
dc.identifier.citation2009 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, VOL IV, PROCEEDINGS, 2009:341-345zh_CN
dc.identifier.otherWOS:000276224200067zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/85713
dc.descriptionConference Name:International Conference on Artificial Intelligence and Computational Intelligence. Conference Address: Shanghai, PEOPLES R CHINA. Time:NOV 07-08, 2009.zh_CN
dc.description.abstractBusiness activity and engineering practice always produce large data sets carrying important information, but because of the data sets' largeness and frequent updating, if we apply the Apriori based Algorithms to them for incremental rules mining, it is not only inefficient, but also either redundant rules would be produced under low threshold of minimal support, which makes users hardly distinguish which rules arc really meaningful, or significant rules with low support in additional data set would possibly lost when the threshold is defined high. Motivated by these, therefore, following genetic principles, and combining with natural immune evolution theory and relevant bionic mechanism, this paper proposes an IOGA (Immune Optimization based Genetic Algorithm) approach for incremental association rules mining to large and frequent updating data sets. Experiment demonstrates the method's efficiency and presents its good performance in pruning redundant rules and discovering meaningful rules, perceiving low support rules in additional data set.zh_CN
dc.language.isoen_USzh_CN
dc.publisherIEEE COMPUTER SOCzh_CN
dc.source.urihttp://dx.doi.org/10.1109/AICI.2009.318zh_CN
dc.titleImmune Optimization based Genetic Algorithm for incremental association rules miningzh_CN
dc.typeConferencezh_CN


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