Title: A utility-based approach for business intelligence to discover beneficial itemsets with or without negative profit in retail business industry
Authors: C. Sivamathi; S. Vijayarani
Addresses: Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India ' Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India
Abstract: Utility mining is defined as discovery of high utility itemsets from the large databases. It can be applied in business intelligence for business decision-making such as arranging products in shelf, catalogue design, customer segmentation, cross-selling etc. In this work a novel algorithm MAHUIM (matrix approach for high utility itemset mining) is proposed to reveal high utility itemsets from a transaction database. The proposed algorithm uses dynamic matrix structure. The algorithm scans the database only once and does not generate candidate itemsets. The algorithm calculates minimum threshold value automatically, without seeking from the user. The proposed algorithm is compared with the existing algorithms like HUI-Miner, D2HUP and EFIM. For handling negative utility values, MANHUIM algorithm is proposed and this is compared with HUINIV. For performance analysis, four benchmark datasets like Connect, Foodmart, Chess and Mushroom are used. The result shows that the proposed algorithms are efficient than the existing ones.
Keywords: utility mining high utility itemsets; individual item utility; transaction utility; automatic threshold selection; profitable transactions; pruned items.
International Journal of Business Intelligence and Data Mining, 2020 Vol.16 No.3, pp.361 - 380
Received: 09 May 2017
Accepted: 10 Oct 2017
Published online: 13 Feb 2020 *