Title: P-IOC-FPMine: an association rule mining algorithm for item constraints based on the MapReduce framework

Authors: Zenan Chu; Wei Wang

Addresses: Department of Computer Science and Information Engineering, Anyang Institute of Technology, Anyang, Henan, China ' Department of Computer Science and Information Engineering, Anyang Institute of Technology, Anyang, Henan, China

Abstract: In the application of big data, one of the most challenging problems is how to consider the requirements of users. To avoid this problem, we proposed IOC-FP-growth. This added user-defined pre-term or post-item constraints into the classic FP-growth algorithm. What's more, a parallel data mining algorithm based on MapReduce, namely P-IOC-FPMine was proposed, which was a low-memory fast association rule mining algorithm. Finally, by evaluating the effectiveness of the method in the public data. The results showed that the IOC-FP-growth method can consider the user's needs for association rules easily. Compared with the FP-growth, Recorder and PNARCMC, we only need to meet the requirements of users to extract accurate rules, which will bring huge advantages for data mining. After parallelisation, the performance of the P-IOC-FPMine was better than FP-growth on the data set. The results showed that the P-IOC-FPMine was more appropriate for handling large-scale data sets.

Keywords: association rule; item constraint; MapReduce; parallel algorithm.

DOI: 10.1504/IJWMC.2022.125536

International Journal of Wireless and Mobile Computing, 2022 Vol.23 No.1, pp.57 - 66

Received: 04 Nov 2021
Received in revised form: 09 May 2022
Accepted: 30 May 2022
Published online: 13 Sep 2022 *

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