 HARUIM: high average recent utility itemset miningMathe John Kenny Kumar and Dipti Rana International Journal of Data Mining, Modelling and Management, 2024, vol. 16, issue 1, 66-100 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. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:16:y:2024:i:1:p:66-100 Access Statistics for this article More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd |
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