An efficient fast algorithm for discovering closed+ high utility itemsets

Sahoo, Jayakrushna; Das, Ashok Kumar; Goswami, Adrijit

doi:10.1007/s10489-015-0740-4

Cited by 18 publications

(8 citation statements)

References 43 publications

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“…The algorithm uses a divide-and-conquer approach to mine the complete set of CHUIs in the database without generating candidates. CHUM [32] proposes a generic utility list (gutility-list) that stores utility information and heuristic information about search space pruning, which is different from the utility list proposed in HUI-Miner [12] because gutility-list can calculate closed itemsets quickly and instantly. ULB-Miner [64] uses the designed utility list buffer structure to efficiently store and retrieve utility lists and reuse the memory during the mining process.…”

Section: List-based Methodsmentioning

confidence: 99%

“…FHM [13] is an enhanced version of the HUI-Miner [12] that uses the same vertical data structure to speed up the mining process. CHUM [32] uses a database vertical representation to speed up the generation of the itemsets closure and calculates the execution time of its utility information without accessing the database, and efficiently generates an order retention generator. MHUI [65] is used to efficiently mine HUIs with multiple minutils.…”

Section: E Data Format-based Methodsmentioning

confidence: 99%

“…An index-based novel algorithm can achieve multi-objective evolution, and explore a variety of top-k high utility patterns to further improve user satisfaction, such as ISR-MOEA [73]. Disadvantages are that it is necessary to scan the database multiple times to mine HUIs, and the memory consumption is large, such as UtilityLevel [58], SPHUTTP [63], CHUM [32], HUI-MMUTID [77]; the algorithm of mining HUIs with multiple minimum utility thresholds may be insensitive to the selection of the minutil, such as MHUI [65]. Specific algorithms are shown in Table 9.…”

Section: E Data Format-based Methodsmentioning

confidence: 99%

“…The algorithm also designs an efficient projection-based pruning strategy to produce a more accurate sequence-utility upper subsequence. CHUM [32] uses an EU-List structure, which is a vertical representation of the database. The vertical data structure is capable of computing closures of the itemsets and efficiently generating candidates.…”

Section: Introductionmentioning

confidence: 99%

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A Survey of Key Technologies for High Utility Patterns Mining

et al. 2020

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Recently, high utility pattern mining (HUPM) is one of the most important research issues in data mining. Because it can consider the non-binary frequency values of items in a transaction and the different profit values of each item. It has been widely used. First of all, this paper briefly describes the related concepts, formulas and examples of application for HUPM. Secondly, the key technologies for HUMP are introduced in detail, and they are divided into main methods including Apriori-based, tree-based, projection-based, list-based, data format-based, and index-based and so on. The paper further compares data sets, uses, advantages and disadvantages of algorithms, laid the foundation for the next research direction. Then, this article outlines the high utility derivative patterns, including high average utility pattern, high utility sequential pattern, and high utility compact pattern and so on. Because static data is difficult to meet the actual needs, this paper summarizes the efficient use of HUPMs' methods over data streams, mainly based on incremental methods, based on the sliding window model methods, based on the time decay model methods and based on the landmark model methods and so on. INDEX TERMS Survey, pattern mining, high utility pattern, data streams, incremental databases.

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Section: List-based Methodsmentioning

confidence: 99%

Section: E Data Format-based Methodsmentioning

confidence: 99%

Section: E Data Format-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Survey of Key Technologies for High Utility Patterns Mining

et al. 2020

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“…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.

240

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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