Effective utility mining with the measure of average utility

Hong, Tzung-Pei; Lee, Cho-Han; Wang, Shyue-Liang

doi:10.1016/j.eswa.2011.01.006

Cited by 105 publications

(58 citation statements)

References 28 publications

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“…Considers the length of itemset as a major factor, and the average utility of X in Tq is defined as au(X, Tq) = i j ∈X∧X⊆Tq q(ij , Tq) × pr(ij )/|X| [70], where k is the number of items in X. Expected/potential utility Measures both probability and utility of a pattern in uncertain databases [71]; the expected support [72] is measured as…”

Section: Average Utilitymentioning

confidence: 99%

“…HAUIM divides the utility of an itemset by its length (the number of items that the itemset contains). Up to now, some interesting works have been extensively studied, such as Apriori-based algorithms [70], projection-based PAI [94], utility-list based HAUI-Miner [95], [96], and other hybrid algorithms with different upper-bound models [96], [97].…”

Section: Mining High Average Utility Itemsetsmentioning

confidence: 99%

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A Survey of Utility-Oriented Pattern Mining

Gan

Lin

Fournier‐Viger

et al. 2021

IEEE Trans. Knowl. Data Eng.

230

148

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The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge. For identifying and evaluating the usefulness of different kinds of patterns, many techniques/constraints have been proposed, such as support, confidence, sequence order, and utility parameters (e.g., weight, price, profit, quantity, etc.). In recent years, there has been an increasing demand for utility-oriented pattern mining (UPM). UPM is a vital task, with numerous high-impact applications, including cross-marketing, e-commerce, finance, medical, and biomedical applications. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of UPM. First, we introduce an in-depth understanding of UPM, including concepts, examples, and comparisons with related concepts. A taxonomy of the most common and state-of-the-art approaches for mining different kinds of high-utility patterns is presented, including Apriori-based, tree-based, projection-based, vertical-/horizontal-data-format-based, and other hybrid approaches. A comprehensive review of advanced topics of existing high-utility pattern mining techniques is offered, with a discussion of their pros and cons. Finally, we present several well-known open-source software packages for UPM. We conclude our survey with a discussion on open and practical challenges in this field.

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Section: Average Utilitymentioning

confidence: 99%

Section: Mining High Average Utility Itemsetsmentioning

confidence: 99%

A Survey of Utility-Oriented Pattern Mining

Gan

Lin

Fournier‐Viger

et al. 2021

IEEE Trans. Knowl. Data Eng.

230

148

View full text Add to dashboard Cite

show abstract

“…Besides, Lin et al () studied a series of problems related to mining HUIs in dynamic databases, such as considering record insertion, record deletion (Lin, Gan, & Hong, ), and record modification (Lin, Gan, & Hong, ). Being different from the traditional definition of HUIM, Hong, Lee, and Wang () introduced the concept of high average‐utility itemset. Recently, several studies related to mining high average‐utility itemsets with various constraints have been published (Kim & Yun, ; Yun & Kim, ).…”

Section: Basic Approaches For Huimmentioning

confidence: 99%

A survey of incremental high‐utility itemset mining

Gan

Lin

Fournier-Viger

et al. 2018

WIREs Data Min & Knowl

129

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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

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“…However, utility mining is based on only the utility of itemset and does not consider their length while the longer length itemset is, the higher utility its value has. The average utility (au) measure was proposed by Hong in [3] where the author considered the length of itemset. However, due to the lack of "downward closure property", the cost of candidate generation for mining high utility itemsets is intolerable in terms of time and memory space.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Mining High Average Utility Itemsets

Nguyen

et al. 2014

Lecture Notes in Computer Science

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Part 2: AlgorithmsInternational audienceData mining is one of exciting fields in recent years. Its purpose is to discover useful information and knowledge from large databases for business decisions and other areas. One engineering topic of data mining is utility mining which discovers high-utility itemsets. An itemset in traditional utility mining considers individual profits and quantities of items in transactions regardless of its length. The average-utility measure is then proposed. This measure is the total utility of an itemset divided by the number of items. Several mining algorithms were also proposed for mining high average-utility itemsets (HAUIs) from a transactional database. However, the number of generated candidates is very large since an itemset is not a HAUI, but itemsets generated from it and others can be HAUIs. Some effective approaches have been proposed to prune candidates and save time. This paper proposes a new method to mine HAUI from transaction databases. The advantage of this method is to reduce candidates efficiently by using HAUI-Tree. A new itemset structure is also developed to improve the speed of calculating the values of itemsets and optimize the memory usage

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Effective utility mining with the measure of average utility

Cited by 105 publications

References 28 publications

A Survey of Utility-Oriented Pattern Mining

A Survey of Utility-Oriented Pattern Mining

A survey of incremental high‐utility itemset mining

A New Method for Mining High Average Utility Itemsets

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