Frequent item sets mining from high-dimensional dataset based on a novel binary particle swarm optimization

• Published in: Journal of Central South University Vol. 23; no. 7; pp. 1700 - 1708
• Main Authors: Zhang, Zhong-jie, Huang, Jian, Wei, Ying
• Format: Journal Article
• Language: English
• Published: Changsha Central South University 01.07.2016
• Subjects: Control Science and Information Engineering; Engineering; Mechanical Engineering; Metallic Materials; data mining; frequent item sets; particle swarm optimization
• ISSN: 2095-2899; 2227-5223
• DOI: 10.1007/s11771-016-3224-8

A novel binary particle swarm optimization for frequent item sets mining from high-dimensional dataset (BPSO-HD) was proposed, where two improvements were joined. Firstly, the dimensionality reduction of initial particles was designed to ensure the reasonable initial fitness, and then, the dynamically dimensionality cutting of dataset was built to decrease the search space. Based on four high-dimensional datasets, BPSO-HD was compared with Apriori to test its reliability, and was compared with the ordinary BPSO and quantum swarm evolutionary (QSE) to prove its advantages. The experiments show that the results given by BPSO-HD is reliable and better than the results generated by BPSO and QSE.