Direct Candidates Generation: A Novel Algorithm for Discovering Complete Share-Frequent Itemsets

Li, Yu-Chiang; Yeh, Jieh-Shan; Chang, Chin‐Chen

doi:10.1007/11540007_67

Cited by 40 publications

(41 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The DCG method is efficient for discovering SH-frequent itemsets [24]. To discover high utility itemsets, like ShFSM, we simply replace the share value of each item with its utility value in the dataset and properly set up the minimum threshold.…”

Section: Utility Mining Using Share Mining Methodsmentioning

confidence: 99%

“…Nevertheless, these predictive methods may not discover some high utility itemsets. Recently, Li et al first developed some efficient approaches, including the FSM, SuFSM, ShFSM, and DCG methods, to identify all SH-frequent itemsets [22][23][24]. In the meanwhile, Liu et al also presented a Two-Phase (TP) method to discover all high utility itemsets [27,28].…”

Section: Tidmentioning

confidence: 99%

“…Liu et al presented a Two-Phase (TP) algorithm for the same purpose [27,28]. Li et al also suggested efficient algorithms such as ShFSM and DCG [23,24]. Table 4 Transaction utility values of the sample database in Table 1 Transaction T01 T02 T03 T04 T05 T06 Tutil(DB) Transaction utility 21 24 30 19 17 11 122 Table 5 All local utility values of 1-itemsets of Table 1 1…”

Section: Existing Algorithmsmentioning

confidence: 99%

“…Therefore, Li et al proposed the Direct Candidates Generation (DCG) algorithm to improve the performance of the mining process [24]. DCG is a level-wise method that DCG maintains an array for each candidate during each pass.…”

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%

See 4 more Smart Citations

Isolated items discarding strategy for discovering high utility itemsets

Yeh

Chang

2008

Data & Knowledge Engineering

Self Cite

248

151

View full text Add to dashboard Cite

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.

show abstract

Section: Utility Mining Using Share Mining Methodsmentioning

confidence: 99%

Section: Tidmentioning

confidence: 99%

Section: Existing Algorithmsmentioning

confidence: 99%

Section: Existing Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Isolated items discarding strategy for discovering high utility itemsets

Yeh

Chang

2008

Data & Knowledge Engineering

Self Cite

248

151

View full text Add to dashboard Cite

show abstract

A survey of incremental high‐utility itemset mining

Gan

Lin

Fournier-Viger

et al. 2018

WIREs Data Min & Knowl

129

View full text Add to dashboard Cite

Traditional association rule mining has been widely studied. But it is unsuitable for real‐world applications where factors such as unit profits of items and purchase quantities must be considered. High‐utility itemset mining (HUIM) is designed to find highly profitable patterns by considering both the purchase quantities and unit profits of items. However, most HUIM algorithms are designed to be applied to static databases. But in real‐world applications such as market basket analysis and business decision‐making, databases are often dynamically updated by inserting new data such as customer transactions. Several researchers have proposed algorithms to discover high‐utility itemsets (HUIs) in dynamically updated databases. Unlike batch algorithms, which always process a database from scratch, incremental high‐utility itemset mining (iHUIM) algorithms incrementally update and output HUIs, thus reducing the cost of discovering HUIs. This paper provides an up‐to‐date survey of the state‐of‐the‐art iHUIM algorithms, including Apriori‐based, tree‐based, and utility‐list‐based approaches. To the best of our knowledge, this is the first survey on the mining task of incremental high‐utility itemset mining. The paper also identifies several important issues and research challenges for iHUIM. WIREs Data Mining Knowl Discov 2018, 8:e1242. doi: 10.1002/widm.1242 This article is categorized under: Algorithmic Development > Association Rules Application Areas > Data Mining Software Tools Fundamental Concepts of Data and Knowledge > Knowledge Representation

show abstract

Efficient Algorithms for High Utility Itemset Mining Without Candidate Generation

Liu

Fournier‐Viger

2019

Studies in Big Data

View full text Add to dashboard Cite

Direct Candidates Generation: A Novel Algorithm for Discovering Complete Share-Frequent Itemsets

Cited by 40 publications

References 14 publications

Isolated items discarding strategy for discovering high utility itemsets

Isolated items discarding strategy for discovering high utility itemsets

A survey of incremental high‐utility itemset mining

Efficient Algorithms for High Utility Itemset Mining Without Candidate Generation

Contact Info

Product

Resources

About