A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets

Liu, Ying; Liao, Wei-keng; Choudhary, Alok

doi:10.1007/11430919_79

Cited by 567 publications

(554 citation statements)

References 6 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%

“…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%

“…To address this urgent problem, Li et al proposed the FSM algorithm, a non-exhaustive search method, to discover all SH-frequent itemsets [22]. 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].…”

Section: Existing Algorithmsmentioning

confidence: 99%

“…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%

“…Recently, Yao et al [36] attempted to build a unified framework for utilitybased measures [11,27,28,32,[34][35][36] that allows the user to select a suitable utility mining tool for a specific application; however, this framework only employs existing tools. Thus, to effectively discover high utility itemsets, the need for efficient algorithms remains urgent.…”

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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Section: Tidmentioning

confidence: 99%

Section: Tidmentioning

confidence: 99%

Section: Existing Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

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

248

139

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Mining high utility itemsets with time‐aware scheduling using Apache Spark

Brahmavar

Venkatarama

Maiya

2022

Concurrency and Computation

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Summary Since the last decade, Market Basket Analysis has been propelled by augmentation of revenue information. Termed as high utility itemset mining (HUIM), this task considers the factors of purchase quantity and unit profit of the items in the transaction database. Although several sequential algorithms to mine HUIs exist, their performance degrades as the database becomes voluminous. Distributed computing solutions such as Apache Hadoop and Apache Spark have proven effective in alleviating this bottleneck. In this regard, the current study develops a parallel workflow to adapt a single‐phase tree‐based algorithm called the single phase utility computation (SPUC) algorithm on a Spark cluster. Based on the time taken to mine individual conditional pattern bases in SPUC, an assignment strategy that partitions the search space across the cluster is proposed in parallel SPUC (PSPUC) algorithm. Experimental evaluation conducted using real and synthetic datasets demonstrate that PSPUC outperforms PHUI‐Growth algorithm. Apart from this, PSPUC in conjunction with the time‐aware assignment strategy converges mining faster than a random assignment of items. A linear speedup of PSPUC is also demonstrated.

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Average utility driven data analytics on damped windows for intelligent systems with data streams

et al. 2021

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In industrial areas, most of databases are dynamic databases, and the volume of the databases has grown with the passage of time. Especially, pattern mining for incremental database needs different approaches from static database because the profit or the accuracy of the previously inserted data can be reduced. Since data is How to cite this article: Kim J, Yun U, Kim H, et al. Average utility driven data analytics on damped windows for intelligent systems with data streams.

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A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets

Cited by 567 publications

References 6 publications

Isolated items discarding strategy for discovering high utility itemsets

Isolated items discarding strategy for discovering high utility itemsets

Mining high utility itemsets with time‐aware scheduling using Apache Spark

Average utility driven data analytics on damped windows for intelligent systems with data streams

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