Quick response data mining model using genetic algorithm

Wenxiang Dou*, Jinglu Hu, Kotaro Hirasawa, Gengfeng Wu

*この研究の対応する著者

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

propose an efficient data mining system for making quick response to users and providing a friendly interface. When data tuples have higher relationship, it could contain long frequent itemsets. If apriori algorithm mines all frequent itemsets in those tuples, its candidate itemsets will become very huge and it has to scan database huge times. Meanwhile, the number of rules mined by the apriori algorithm is huge. Our method avoids mining rules through huge candidate itemsets, just mines maximal frequent itemsets and scans the database for the frequent itemsets users are interested in. First, use GA to mine the maximal frequent itemsets and show them to users. Second, let users pick up one to deduce the association rules. Final, scan the database for the real support and confidence and show them to users. So, our method can not only save many times scanning the database and make quick response to users, but provide a friendly interface that let users select his interesting rules to mine.

本文言語English
ホスト出版物のタイトルProceedings of SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology
ページ1214-1219
ページ数6
DOI
出版ステータスPublished - 2008 12月 1
イベントSICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology - Tokyo, Japan
継続期間: 2008 8月 202008 8月 22

出版物シリーズ

名前Proceedings of the SICE Annual Conference

Conference

ConferenceSICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology
国/地域Japan
CityTokyo
Period08/8/2008/8/22

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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