The study on genetic algorithm on mining quantitative association rules

Yue Wang; Liang Li

doi:10.1117/12.664644

2 May 2006 The study on genetic algorithm on mining quantitative association rules

Yue Wang, Liang Li

Proceedings Volume 6042, ICMIT 2005: Control Systems and Robotics; 604225 (2006) https://doi.org/10.1117/12.664644
Event: ICMIT 2005: Merchatronics, MEMS, and Smart Materials, 2005, Chongqing, China

Abstract

With the development of the internet and the application of databases, seas of storage of data have become available. How to use the data for humans is the task of data mining. But in the process of data mining, a problem often encountered is that mining association rules on the quantitative attributes in RDBMS (Relational Database Management System) or Web logs. A genetic algorithm is proposed in the present paper to solve the clustering problem which can be solved by FCM (Fuzzy Clustering Method), so as to avoid the local optimization that often occurs in FCM. The quantitative attributes can be converted into categorical attributes, and then the categorical attributes are mapped into Boolean attributes, so that many association algorithms can be used to mine significant association rules.

Citation Download Citation

Yue Wang and Liang Li "The study on genetic algorithm on mining quantitative association rules", Proc. SPIE 6042, ICMIT 2005: Control Systems and Robotics, 604225 (2 May 2006); https://doi.org/10.1117/12.664644

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