Article: An irregular CLA-based novel frequent pattern mining approach Journal: International Journal of Data Mining, Modelling and Management (IJDMMM) 2024 Vol.16 No.3 pp.268 - 292 Abstract: Frequent itemset mining has received a lot of attention in the field of data mining. Its main objective is to find groups of items that consistently appear together in datasets. Even while frequent itemset mining is useful, the algorithms for mining frequent itemsets have quite high resource requirements. In order to optimise the time and memory needs, a few improvements have been made in recent years. This study proposes CellFPM, a straightforward yet effective cellular learning automata-based method for finding frequent itemset occurrences. It works efficiently with large datasets. The efficiency of the proposed approach in time and memory requirements has been evaluated using benchmark datasets explicitly designed for performance measure. The varying size and density of the test datasets have confirmed the scalability of the suggested method. The findings show that CellFPM consistently surpasses the leading algorithms in terms of runtime and memory usage, particularly memory usage mostly. Inderscience Publishers - linking academia, business and industry through research

Title: An irregular CLA-based novel frequent pattern mining approach

Authors: Moumita Ghosh; Sourav Mondal; Harshita Moondra; Dina Tri Utari; Anirban Roy; Kartick Chandra Mondal

Addresses: Department of Information Technology, Jadavpur University, Kolkata, India ' Department of Information Technology, Jadavpur University, Kolkata, India ' Department of Information Technology, Jadavpur University, Kolkata, India ' Department of Statistics, Universitas Islam Indonesia, Indonesia ' Department of Environment, West Bengal Biodiversity Board, India ' Department of Information Technology, Jadavpur University, Kolkata, India

Abstract: Frequent itemset mining has received a lot of attention in the field of data mining. Its main objective is to find groups of items that consistently appear together in datasets. Even while frequent itemset mining is useful, the algorithms for mining frequent itemsets have quite high resource requirements. In order to optimise the time and memory needs, a few improvements have been made in recent years. This study proposes CellFPM, a straightforward yet effective cellular learning automata-based method for finding frequent itemset occurrences. It works efficiently with large datasets. The efficiency of the proposed approach in time and memory requirements has been evaluated using benchmark datasets explicitly designed for performance measure. The varying size and density of the test datasets have confirmed the scalability of the suggested method. The findings show that CellFPM consistently surpasses the leading algorithms in terms of runtime and memory usage, particularly memory usage mostly.

Keywords: cellular learning automata; CLA; frequent itemsets; data mining; knowledge discovery.

DOI: 10.1504/IJDMMM.2024.140536

International Journal of Data Mining, Modelling and Management, 2024 Vol.16 No.3, pp.268 - 292

Received: 15 Jan 2023
Accepted: 29 Aug 2023
Published online: 22 Aug 2024 *

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Title: An irregular CLA-based novel frequent pattern mining approach

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