Title: Deep mining method for high-dimensional big data based on association rule

Authors: Shu Xu

Addresses: College of Information and Electronic Engineering, Hunan City University, Yiyang, Hunan, 413000, China

Abstract: Existing high-dimensional deep data mining methods have the problems of low precision and high energy consumption. Therefore, a deep mining method of high-dimensional big data based on association rules is proposed. Ealat algorithm is used to change the format of high-dimensional large data set. On this basis, MapReduce computing model is introduced to divide parallel tasks into map and reduce phases to realise the construction of operation platform. Hadoop's distributed file system is used to store distributed data. The input and output of the algorithm are converted into the form required by the MapReduce computing model to realise the deep mining of high-dimensional big data. Experimental results show that this method has higher mining accuracy and lower energy consumption. The result of practical application is good.

Keywords: association rule; high-dimensional big data; deep mining.

DOI: 10.1504/IJIPT.2021.117412

International Journal of Internet Protocol Technology, 2021 Vol.14 No.3, pp.147 - 154

Received: 12 Mar 2019
Accepted: 26 Oct 2019
Published online: 06 Sep 2021 *

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