Isolated items discarding strategy for discovering high utility itemsets

Abstract

Traditional methods of association rule mining consider the appearance of an item in a transaction, whether or not it is purchased, as a binary variable. However, customers may purchase more than one of the same item, and the unit cost may vary among items. Utility mining, a generalized form of the share mining model, attempts to overcome this problem. Since the Apriori pruning strategy cannot identify high utility itemsets, developing an efficient algorithm is crucial for utility mining. This study proposes the Isolated Items Discarding Strategy (IIDS), which can be applied to any existing level-wise utility mining method to reduce candidates and to improve performance. The most efficient known models for share mining are ShFSM and DCG, which also work adequately for utility mining as well. By applying IIDS to ShFSM and DCG, the two methods FUM and DCG+ were implemented, respectively. For both synthetic and real datasets, experimental results reveal that the performance of FUM and DCG+ is more efficient than that of ShFSM and DCG, respectively. Therefore, IIDS is an effective strategy for utility mining.

Handling Dynamic Weights in Weighted Frequent Pattern Mining

“…Therefore, the weight value of each item was heuristically chosen to be between 0.1 and 0.9, and randomly generated using a log-normal distribution. Some other pattern mining research [26], [27] has adopted the same technique. Figure 4 shows the weight distribution of 2000 distinct items using the log-normal distribution.…”

“…Their useful comments have played a significant role in improving the quality of this work. We are also grateful to professor Yu-Chiang Li [27] for his help in finding the real-life Chain-store dataset with real weight values in NU-MineBench 2.0 [24].…”

“…This dataset called Chain-store was taken from a major chain in California and contains 1,112,949 transactions and 46,086 distinct items [24], [27]. We have taken real weight values for items from their price table.…”

Recommendation System for High Utility Itemsets over Incremental Dataset

“…Utility mining [5,29,30] was proposed to solve the above mentioned problem by considering the factors like cost, profit or other factors of users" interest. Thus the issue of high utility itemsets mining is raised and many studies [4,9,17,19,20,25,27] have addressed this problem. Liu, Liao & Choudhary [19,20] proposed the two phase utility mining algorithm for efficiently extracting all high utility itemsets based on the downward closure property.…”

“…Although two phase algorithm reduces search space, it still generates too many candidates and requires multiple database scans. To overcome this problem, Li, Yeh & Chang [17] proposed an isolated items discarding strategy (IIDS) to reduce the number of candidates. To efficiently generate HTWI"s and to avoid multiple scans, Ahmed, Tanbeer, Jeong & Lee [4] proposed a tree based algorithm named IHUP.…”