Title: A novel classification-based parallel frequent pattern discovery model for decision making and strategic planning in retailing

Authors: Rajiv Senapati

Addresses: Department of Computer Science and Engineering, SRM University, Amaravati, Andhra Pradesh, India

Abstract: The exponential growth of retail transactions with a variety of customers having different interests makes the pattern mining problem trivial. Hence this paper proposes a novel model for mining frequent patterns. As per the proposed model the frequent pattern discovery is carried out in three phases. In the first phase, dataset is divided into n partitions based on the time stamp. In the second phase, clustering is performed in each of the partitions parallelly to classify the customers as HIG, MIG, and LIG. In the third phase, proposed algorithm is applied on each of the classified groups to obtain frequent patterns. Finally, the proposed model is validated using a sample dataset and experimental results are presented to explain the capability and usefulness of the proposed model and algorithm. Further, the proposed algorithm is compared with the existing algorithm and it is observed that the proposed algorithm performs better in terms of time complexity.

Keywords: data mining; frequent pattern; association rule; classification; algorithm; decision making; retailing.

DOI: 10.1504/IJBIDM.2023.132579

International Journal of Business Intelligence and Data Mining, 2023 Vol.23 No.2, pp.184 - 200

Received: 09 Dec 2021
Accepted: 03 Mar 2022
Published online: 30 Jul 2023 *

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