Title: Research on parallel association rule mining of big data based on an improved K-means clustering algorithm

Authors: Li Hao; Tuanbu Wang; Chaoping Guo

Addresses: College of Information Engineering, Xijing University, Xi'an 710123, China ' College of Information Engineering, Xijing University, Xi'an 710123, China ' College of Information Engineering, Xijing University, Xi'an 710123, China

Abstract: In order to overcome the problems of time-consuming, low-precision and redundant rules in association rule mining of big data, a parallel association rule mining method based on an improved K-means clustering algorithm is proposed. Establish a data object criterion function and optimise k-means clustering algorithm. The improved K-means clustering algorithm is used to cluster big data and improve the efficiency of mining association rules. This paper introduces the matter-element theory of extension, combines matter-element theory and database, and constructs the matter-element relation database model of extension to realise the mining of parallel association rules in big data on the basis of extension. Redundant algorithms and equivalent transformations are used to eliminate redundant association rules. The experimental results show that the proposed method has high mining efficiency, high mining accuracy, and high rule association, which proves that the proposed method has better application performance.

Keywords: K-means clustering algorithm; association rules; data mining; redundancy algorithm; equivalence transformation.

DOI: 10.1504/IJAACS.2023.131622

International Journal of Autonomous and Adaptive Communications Systems, 2023 Vol.16 No.3, pp.233 - 247

Received: 28 Apr 2020
Accepted: 03 Sep 2020
Published online: 21 Jun 2023 *

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