Mining closed+ high utility itemsets without candidate generation

Wu, Chengwei; Fournier-Viger, Philippe; Gu, Jiwei; Tseng, Vincent S.

doi:10.1109/taai.2015.7407089

Cited by 42 publications

(74 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…EFIM-Closed relies on two new upper bounds, named local utility and sub-tree utility, to prune the search space, and a FUC technique to calculate these upper bounds efficiently. Inspired by utility-list [24], some more efficient one-phase algorithms have been proposed to address this interesting issue, such as CHUI-Miner [117] and CHUM [118].…”

Section: Concise Representations Of Utility Patternsmentioning

confidence: 99%

A Survey of Utility-Oriented Pattern Mining

Gan

Lin

Fournier‐Viger

et al. 2021

IEEE Trans. Knowl. Data Eng.

Self Cite

226

148

View full text Add to dashboard Cite

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.

show abstract

Section: Concise Representations Of Utility Patternsmentioning

confidence: 99%

A Survey of Utility-Oriented Pattern Mining

Gan

Lin

Fournier‐Viger

et al. 2021

IEEE Trans. Knowl. Data Eng.

Self Cite

226

148

View full text Add to dashboard Cite

show abstract

“…In last few years, HUIs mining came in trend to solve the problems of items that have quantity and weight (profit or price; Ahmed et al, ; Chan et al, ; Chu et al, ; Erwin et al, ; Fournier‐Viger et al, ; Fournier‐Viger, Zida, Lin, Wu, & Tseng, ; Krishnamoorthy, ; Liu and Qu, ; Liu et al, a; Liu et al, b; Song et al, ; Tseng et al, ; Wu, Fournier‐Viger, Gu, & Tseng , Yao et al, ; Yun, Ryang, & Ryu, ; Zida, Fournier‐Viger, Lin, Wu, & Tseng, ; Zida et al, ). HUIs mining algorithms can be categorized into two types: two‐phase and one‐phase algorithms.…”

Section: Related Workmentioning

confidence: 99%

Mining of high‐utility itemsets with negative utility

et al. 2018

View full text Add to dashboard Cite

High‐utility itemset (HUI) mining is an important tasks during data mining. Recently, many algorithms have been proposed to discover HUIs. Most of the algorithms work only for itemsets with positive utility values. However, in the real world, items are found with both positive and negative utility values. To address this issue, we propose an algorithm named Efficient High‐utility Itemsets mining with Negative utility (EHIN) to find all HUIs with negative utility. EHIN utilises 2 new upper bounds for pruning, named revised subtree and revised local utility. To reduce dataset scans, the proposed algorithm uses transaction merging and dataset projection techniques. An array‐based utility‐counting technique is also utilised to calculate upper‐bound efficiently. EHIN utilises various properties and pruning strategies to mine HUIs with negative utility. The experimental results show that the proposed algorithm is 28 times faster, and it consumes up to 10 times less memory than the state‐of‐the‐art algorithm FHN. Moreover, a key advantage is that EHIN always performs better for dense datasets.

show abstract

“…Overall, these are efficient approaches for extracting CHUIs and generators directly from a database. Wu et al (2015) provided a solution to mine Closed + High Utility Itemsets, compact and lossless representations of HUIs. This approach helps to resolve the issue of large numbers of candidates being generated, and thus has better performance in both runtime and memory consumption.…”

Section: Chuis and Generators Miningmentioning

confidence: 99%

“…A CHUI is a HUI having no proper supersets which are HUIs and exists in the same transactions set (Tseng, Wu, Fournier-Viger, & Yu, 2015). In HUIM, there have been many investigations on extracting CHUIs and generators (Fournier-Viger, Gomariz, Soltani, & Gueniche, 2014;Sahoo, Das, & Goswami, 2015;Wu, Fournier-Viger, Gu, & Tseng, 2015).…”

Section: Introductionmentioning

confidence: 99%

An efficient approach for mining closed high utility itemsets and generators

Mai¹,

Nguyen

2017

Journal of Information and Telecommunication

View full text Add to dashboard Cite

Mining closed high utility itemsets (CHUIs) serves as a compact and lossless representation of high utility itemsets (HUIs). CHUIs and their generators are useful in analytical and recommendation systems. In this paper, we introduce a lattice approach to extract CHUIs and their generators from a set of HUIs quickly. The experimental results show that mining CHUIs and their generators from a lattice of HUIs is efficient in both runtime and memory usage. ARTICLE HISTORY

show abstract

Mining closed+ high utility itemsets without candidate generation

Cited by 42 publications

References 14 publications

A Survey of Utility-Oriented Pattern Mining

A Survey of Utility-Oriented Pattern Mining

Mining of high‐utility itemsets with negative utility

An efficient approach for mining closed high utility itemsets and generators

Contact Info

Product

Resources

About