Title: Mining closed high utility itemsets using sliding window infrastructure model over data stream

Authors: Ponna Mahesh Kumar; P. Srinivasa Rao

Addresses: Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Kakinada, AP, India ' Department of Computer Science and Engineering, MVGR College of Engineering (Autonomous), AP, India

Abstract: A group of products that has utility values and that are sold together greater than a preset lowest utility cut-off is produced by mining high-utility itemsets. These itemsets' profit units have external and internal usefulness values. In each transaction, the quantity of each item sold, respectively, is considered to determine the utilities of these itemsets. As a result, assessing an itemset's high utility is symmetrically dependent on all of its internal and external utilities. Both utilities contributed equally, and there are two key deciding considerations. First, selling groupings of low-external utility commodities generally meets the minimal utility requirement. Regular itemset mining can help find such itemsets. Second, numerous high-utility itemsets are created; thus, some interesting or significant ones may be omitted. This study applies an asymmetric technique that overlooks interior utility counts to discover those with considerable external utility counts. Two genuine datasets showed that external utility values strongly affect high utility itemsets. This study also shows that high minimal utility threshold values and a faster method increase this influence.

Keywords: high utility itemset; sliding window; information extraction; high-utility itemset mining; HUIM; itemset mining.

DOI: 10.1504/IJCIS.2024.141442

International Journal of Critical Infrastructures, 2024 Vol.20 No.5, pp.447 - 462

Received: 15 Jan 2023
Accepted: 03 Mar 2023
Published online: 13 Sep 2024 *

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