Bahui

Song, Wei; Yu, Liu; Li, Jinhong

doi:10.4018/ijdwm.2014010101

Cited by 54 publications

(9 citation statements)

References 31 publications

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“…In [36], HMiner was improved by using utility information storage. Methods such as BAHUI [37], the HUIM-BPSO sign [38], MinHUIs [39], and FHM+ [40] were designed for HIUM; however, they were found to be susceptible to large real-time continuous data. Unlike HIUM methods, high average-utility itemset mining methods use average-utility values to reduce reliance on length constraints.…”

Section: System Model and Methodsmentioning

confidence: 99%

“…The HIUM method applies a utility factor that signifies the total profit of an itemset to identify a set of high-utility items. Numerous state-of-the-art HIUM algorithms [12][13][14][15] generate a large volume of candidate itemsets and impose computational costs and delays. However, pruning insignificant itemsets (i.e., items with a low frequency) can reduce the search space and improve performance [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Sized cumulative Summary Structure Driven Light Weight in Frequent Closed Itemset Mining to Increase High Utility

Siva

Chaudhari

2023

JICCE

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High-utility itemset mining (HIUM) has emerged as a key data-mining paradigm for object-of-interest identification and recommendation systems that serve as frequent itemset identification tools, product or service recommendation systems, etc. Recently, it has gained widespread attention owing to its increasing role in business intelligence, top-N recommendation, and other enterprise solutions. Despite the increasing significance and the inability to provide swift and more accurate predictions, most at-hand solutions, including frequent itemset mining, HUIM, and high average-and fast high-utility itemset mining, are limited to coping with real-time enterprise demands. Moreover, complex computations and high memory exhaustion limit their scalability as enterprise solutions. To address these limitations, this study proposes a model to extract high-utility frequent closed itemsets based on an improved cumulative summary list structure (CSLFC-HUIM) to reduce an optimal set of candidate items in the search space. Moreover, it employs the lift score as the minimum threshold, called the cumulative utility threshold, to prune the search space optimal set of itemsets in a nested-list structure that improves computational time, costs, and memory exhaustion. Simulations over different datasets revealed that the proposed CSLFC-HUIM model outperforms other existing methods, such as closed-and frequent closed-HUIM variants, in terms of execution time and memory consumption, making it suitable for different mined items and allied intelligence of business goals.

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Section: System Model and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Sized cumulative Summary Structure Driven Light Weight in Frequent Closed Itemset Mining to Increase High Utility

Siva

Chaudhari

2023

JICCE

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“…The task of mining high-utility itemset is to discover the complete set of itemsets with a utility that satisfies a prespecified minimum utility threshold: u(X) ≥ minutil. The discovered itemsets are called high-utility itemsets (Liu et al, 2005); Fournier-Viger et al, 2020; Tseng et al, 2013;Song et al, 2014;Fournier-Viger et al, 2018).…”

Section: Definition 1 (Transaction Database)mentioning

confidence: 99%

A General Method for mining high-Utility itemsets with correlated measures

Hùng

Tung

2021

Journal of Information and Telecommunication

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Discovering high-utility itemsets from a transaction database is one of the important tasks in High-Utility Itemset Mining (HUIM). The discovered high-utility itemsets (HUIs) must meet a user-defined given minimum utility threshold. Several methods have been proposed to solve the problem efficiently. However, they focused on exploring and discovering the set of HUIs. This research proposes a more generalized approach to mine HUIs using any user-specified correlated measure, named the General Method for Correlated High-utility itemset Mining (GMCHM). This proposed approach has the ability to discover HUIs that are highly correlated, based on the all_confidence and bond measures (and 38 other correlated measures). Evaluations were carried out on the standard datasets for HUIM, such as Accidents, BMS_utility and Connect. The results proved the high effectiveness of GMCHM in terms of running time, memory usage and the number of scanned candidates.

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“…The two-phase algorithm is the first complete algorithm for HUIM, and the TWU measure makes the mining process easier because it is antimonotonic. Many algorithms [32][33][34][35][36][37] followed up by applying the TWU measure. However, although the TWU measure can be utilized to prune the search space and is easy to count, it has a loose upper bound on the utility of items.…”

Section: Related Work a Huimmentioning

confidence: 99%

FCHUIM: Efficient Frequent and Closed High-Utility Itemsets Mining

et al. 2020

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Mining a closed high-utility itemset is a prevalent research task in analyzing transaction databases. However, numerous target itemsets are generated in the closed high-utility itemset mining task. As a result, too many closed high-utility itemsets (CHUIs) will not only increase the time and memory consumption but also make it difficult for users to analyze the results. To address this problem, this paper proposes an efficient algorithm called FCHUIM, which is used to mine a concise representation called the frequent closed high-utility itemset (FCHUI). FCHUIM is based on a total summary list structure for storing and retrieving utility lists without repeatedly scanning the database. In addition, the proposed algorithm uses an effective upper bound on the utility of items to efficiently reduce the search space. Furthermore, a precheck method and a nested list structure are also introduced in this work to quickly discover FCHUIs. To test the performance of FCHUIM, we conduct extensive experiments on real-life and synthetic databases. We also select two state-of-the-art algorithms to compare with FCHUIM: CHUI-Miner and CLS-Miner. The results show that FCHUIM performs well on databases, whether the databases are dense or sparse.

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Bahui

Cited by 54 publications

References 31 publications

Multi-Sized cumulative Summary Structure Driven Light Weight in Frequent Closed Itemset Mining to Increase High Utility

Multi-Sized cumulative Summary Structure Driven Light Weight in Frequent Closed Itemset Mining to Increase High Utility

A General Method for mining high-Utility itemsets with correlated measures

FCHUIM: Efficient Frequent and Closed High-Utility Itemsets Mining

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