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

  • Mechanical Engineering, Control Science and Information Engineering
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Abstract

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.

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Authors and Affiliations

  1. College of Mechatronics Engineering and Automation, National University of Defense Technology, Changsha, 410073, China

    Zhong-jie Zhang  (张中杰), Jian Huang  (黄健) & Ying Wei  (卫莹)

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  1. Zhong-jie Zhang  (张中杰)
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  2. Jian Huang  (黄健)
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  3. Ying Wei  (卫莹)
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Correspondence to Jian Huang  (黄健).

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Zhang, Zj., Huang, J. & Wei, Y. Frequent item sets mining from high-dimensional dataset based on a novel binary particle swarm optimization. J. Cent. South Univ. 23, 1700–1708 (2016). https://doi.org/10.1007/s11771-016-3224-8

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  • DOI: https://doi.org/10.1007/s11771-016-3224-8

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