Algorithms for Mining Share Frequent Itemsets Containing Infrequent Subsets

Barber, Brock; Hamilton, Howard J.

doi:10.1007/3-540-45372-5_31

Cited by 31 publications

(54 citation statements)

References 8 publications

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“…However, such a method is too time-consuming for a large dataset environment. Several heuristic methods have been proposed to accelerate the discovery of high utility (or SH-frequent) itemsets, such as the MEU (UMining_H) [27,28,34,35], SIP, CAC, and IAB [4,6] methods. Nevertheless, these predictive methods may not discover some high utility itemsets.…”

Section: Tidmentioning

confidence: 99%

“…Share mining has been proposed to overcome the shortcomings of traditional data mining, which overlooks the variance in sale quantity and price/profitability among items in a transaction [6]. Utility mining, a generalized form of the share mining model, is based on measuring internal and external utilities [35].…”

Section: Formal Description Of Utility Miningmentioning

confidence: 99%

“…Exhaustive search methods, such as ZP and ZSP [4,6], can discover all high utility itemsets in a database but may be excessively time-consuming for real-world applications. On the other hand, predictive approaches generally cannot ensure that the mining result contains the complete set of high utility itemsets [5,6,34,35].…”

Section: Existing Algorithmsmentioning

confidence: 99%

“…Carter et al [10] propose the share-confidence model to discover useful knowledge about numerical attributes associated with items in a transaction. Several other methods have since been proposed to efficiently discover share-frequent (SH-frequent) itemsets with infrequent subsets [4][5][6]17,18,[22][23][24]. Yao et al [34,35] generalize the share-confidence model [6] to develop the conventional utility mining model.…”

Section: Introductionmentioning

confidence: 99%

“…Several other methods have since been proposed to efficiently discover share-frequent (SH-frequent) itemsets with infrequent subsets [4][5][6]17,18,[22][23][24]. Yao et al [34,35] generalize the share-confidence model [6] to develop the conventional utility mining model. This model can be used to measure the utility of an itemset in terms of net profit, total cost, or time spent [27,28,34,35].…”

Section: Introductionmentioning

confidence: 99%

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Isolated items discarding strategy for discovering high utility itemsets

Yeh

Chang

2008

Data & Knowledge Engineering

248

139

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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.

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