Title: Deep neural learned bipolar sigmoid association rule mining for discovering high frequent and utility itemsets

Authors: R. Savitha; V. Baby Deepa

Addresses: Department of Computer Science, Government Arts College (Autonomous), Karur, India ' Department of Computer Science, Government Arts College (Autonomous), Karur, India

Abstract: This paper presents a new deep neural learned bipolar sigmoid association rule mining (DNLBSARM) algorithm to improve the mining performance of frequent and utility itemsets (FUI) when considering large transactional data as input. The DNLBSARM is introduced by combining the deep neural learning and bipolar sigmoid association rule generation concepts. Initially, the input layer in DNLBSARM gets large number of items as input to perform mining process. Subsequently, hidden layers in DNLBSARM perform deep analysis where the support and utility value of each itemsets in big database is significantly measured. Finally, output layer in DNLBSARM used bipolar sigmoid association rule with the aiming at accurately discovering and mining top frequent and maximum profited itemsets in massive dataset with minimal amount of time utilisation. From that, DNLBSARM obtained improved extraction performance to find the top user interested and profited itemsets as compared to existing works. The experimental evaluation of DNLBSARM is conducted using parameters such as accuracy, time complexity, and false positive rate by considering various numbers of input items. The testing results demonstrated that the proposed DNLBSARM provides better performance in terms of higher accuracy and lower complexity for extracts the FI and HUT when compared to conventional research works.

Keywords: association rule; bipolar sigmoid activation function; BSAF; deep learning; frequent itemsets; utility itemsets.

DOI: 10.1504/IJCVR.2026.151537

International Journal of Computational Vision and Robotics, 2026 Vol.16 No.2, pp.257 - 271

Received: 19 Dec 2023
Accepted: 12 Jan 2024
Published online: 05 Feb 2026 *

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