An effective parallel approach for genetic-fuzzy data mining

作者：

Highlights：

• We extend our previous work by using the master–slave parallel architecture to dynamically adapt membership functions.

• We use the adapted to deal with quantitative transactions in fuzzy data mining.

• The time complexities for both sequential and parallel genetic-fuzzy mining algorithm have been analyzed.

• The experiments made for showing the performance of the proposed approaches.

摘要

Highlights•We extend our previous work by using the master–slave parallel architecture to dynamically adapt membership functions.•We use the adapted to deal with quantitative transactions in fuzzy data mining.•The time complexities for both sequential and parallel genetic-fuzzy mining algorithm have been analyzed.•The experiments made for showing the performance of the proposed approaches.

论文关键词：Data mining,Fuzzy set,Genetic algorithm,Parallel processing,Association rule

论文评审过程：Available online 13 August 2013.

论文官网地址：https://doi.org/10.1016/j.eswa.2013.07.090