Title: Colossal pattern extraction using optimised length constraints based on differential evolutionary arithmetic optimisation algorithm

Authors: T. Sreenivasula Reddy; R. Sathya; Mallikharjunarao Nuka

Addresses: Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University, Annamalai Nagar, Chidambaram, Tamil Nadu, 608002, India ' Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar, Chidambaram, Tamil Nadu, 608002, India ' Department of Computer Applications, Annamacharya Institute of Technology and Sciences, Rajampet, YSR Kadapa, Andhra Pradesh, 516115, India

Abstract: Extracting large amounts of information and knowledge from a large database is a trivial task. Existing bulk item mining algorithms for an extensive database are systematic and mathematically expensive and cannot be used for large-scale mining with interruptions. In this paper, the problem of mining the colossal patterns (CPs) is solved by using optimised length constraints (LCs). First, we describe the minimum LC and maximum LC problems and connect them to the optimal LC by identifying the optimal threshold values. Here, the differential evolutionary arithmetic optimisation algorithm (DAOA) is used to find the optimal threshold values of the constraints and extract the colossal patterns. The effectiveness of the proposed algorithm is proven by various experiments using different biological datasets.

Keywords: biological databases; colossal patterns; differential evolutionary arithmetic optimisation algorithm; DAOA; massive data itemset; optimised length constraints.

DOI: 10.1504/IJICT.2024.137927

International Journal of Information and Communication Technology, 2024 Vol.24 No.3, pp.289 - 304

Received: 10 Jan 2022
Accepted: 24 Jan 2022
Published online: 11 Apr 2024 *

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