Efficiently updating the discovered high average-utility itemsets with transaction insertion. (June 2018)

• Record Type: Journal Article
• Authors: Lin, Jerry Chun-Wei; Ren, Shifeng; Fournier-Viger, Philippe; Pan, Jeng-Shyan; Hong, Tzung-Pei
• Is Part Of: Engineering applications of artificial intelligence. Volume 72(2018)
• Page Start: 136
• Page End: 149
• Publication Date: 2018-06
• Subjects: High average-utility itemsets -- FUP -- Dynamic -- Insertion -- Incremental; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2018.03.021
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: High-utility itemset mining (HUIM) is an extension of frequent-itemset mining (FIM) but considers the unit profit and quantity of items to discover the set of high-utility itemsets (HUIs). Traditionally, the utility of an itemset is the summation of the utilities of the itemset in all the transactions regardless of its length. This approach is, however, inappropriate in real-world applications since the utility of the itemset increases along with the number of items within it. High average-utility itemset mining (HAUIM) was designed to provide more reasonable utility measure by taking the size of the itemset into account. Existing algorithms can only handle, however, the static database and unsuitable for the dynamic environment since the size of data is frequently changed in real-life situations. In this paper, an incremental high-average utility pattern mining (IHAUPM) algorithm is presented to handle the incremental database with transaction insertion. The well-known fast updated (FUP) concept in the FIM is modified to adopt the designed algorithm, thus efficiently updating the discovered HAUIs. Based on the designed model for HAUIM with transaction insertion, the proposed IHAUPM algorithm can easily only handle the inserted transactions. Experiments are carried on six datasets and the results showed that the designed algorithm has better performance than the state-of-the-art algorithms performing in the batch manner.