Title: HARUIM: high average recent utility itemset mining

Authors: Mathe John Kenny Kumar; Dipti Rana

Addresses: Department of Computer Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India ' Department of Computer Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India

Abstract: High utility itemset mining (HUIM) discovers itemsets that are profitable in nature. Previously, the recency of an itemset was determined by adding the recency of each transaction of an itemset. A major disadvantage of this method is that some transactions of an itemset which are very recent can cause the whole itemset to be recent. To overcome this limitation, we present a novel measure called average recency to mine recent and high utility itemsets. Average recency upper-bound (arub) and estimated recency co-occurrence structure (ERCS) are proposed to prune unpromising itemsets. A variation of list structure known as average recent utility list (ARUL) has been created to hold data regarding utility and recency of itemsets. Through a series of comprehensive experimentation carried out on both real as well as synthetic datasets, it has been demonstrated that the proposed system surpasses the baseline algorithm in runtime, memory utilisation, and candidate generation.

Keywords: data mining; high utility itemset mining; HUIM; recency; average recency; list structure; pattern mining; EUCS; knowledge engineering; candidate generation.

DOI: 10.1504/IJDMMM.2024.136217

International Journal of Data Mining, Modelling and Management, 2024 Vol.16 No.1, pp.66 - 100

Received: 24 Nov 2022
Accepted: 19 Mar 2023
Published online: 22 Jan 2024 *