Title: HCP miner: an efficient heuristic-based clustering method for discovering colossal frequent patterns from high dimensional databases

Authors: T. Sreenivasula Reddy; R. Sathya; Mallikhanjuna Rao 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, Chidambaram, Tamil Nadu, 608002, India ' Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Rajempet, Andhrapradesh, 516115, India

Abstract: This paper presents an efficient heuristic-based clustering method for colossal frequent patterns discovery from the high dimensional databases (HCP miner). The HCP miner avoids exhaustive level-wise pattern tree traversal and quickly mines colossal patterns from the high dimensional databases. To achieve this, our approach constructs the sub-patterns using a lattice array and applies the binary clustering over the sub-patterns initially. While constructing the sub-patterns using a lattice array, it uses the support values. These sub-patterns are explored as conditional patterns by estimating core patterns using heuristic measures to minimise the searching time during the database scan. Finally, colossal cluster is constructed from which colossal patterns are discovered. We perform the experiments on various high dimensional databases using different performance metrics. Our experiments shows that, the proposed HCP miner achieves prominent and efficient results for mining. In addition, these analysis of results reveals that the HCP miner algorithm outperforms with CoreFusion, colossal pattern miner (CPM) in diverse aspects.

Keywords: data mining; big data; frequent pattern mining; FPM; high dimensional database; FIMI dataset; backtracking search.

DOI: 10.1504/IJESMS.2023.133951

International Journal of Engineering Systems Modelling and Simulation, 2023 Vol.14 No.4, pp.186 - 196

Received: 07 Mar 2021
Accepted: 28 Jan 2022
Published online: 06 Oct 2023 *

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