Mining high-utility itemsets based on particle swarm optimization

Lin, Jerry Chun‐Wei; Yang, Libo; Fournier-Viger, Philippe; Wu, Jimmy Ming-Thai; Hong, Tzung-Pei; Wang, Leon Shyue-Liang; Zhan, Justin

doi:10.1016/j.engappai.2016.07.006

Cited by 104 publications

(40 citation statements)

References 20 publications

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“…The PSO algorithm is initialised with a random generated population (initial generation) and the optimal solution is obtained by updating the initial generation step by step. Each particle (solution) in the PSO algorithm moves in the search space based on the previous velocity of the particle, the best solution found in the population (

gbest

), and the best solution achieved by the particle (

pbest

); a set of objective functions is used to determine the performance of each solution [12, 13]. More information about the PSO algorithm can be found in [14–16].…”

Section: Methodsmentioning

confidence: 99%

Gene expression feature selection for prostate cancer diagnosis using a two‐phase heuristic–deterministic search strategy

et al. 2018

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gbest

), and the best solution achieved by the particle (

pbest

); a set of objective functions is used to determine the performance of each solution [12, 13]. More information about the PSO algorithm can be found in [14–16].…”

Section: Methodsmentioning

confidence: 99%

Gene expression feature selection for prostate cancer diagnosis using a two‐phase heuristic–deterministic search strategy

et al. 2018

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Jing Bi . the limitations of FIM, a high-utility itemset mining (HUIM) method was designed to discover high-utility itemsets from databases [9], [10]. A high utility itemset (HUI) refers to an itemset whose utility value is not less than the minimum utility threshold set by the user.…”

Section: Introductionmentioning

confidence: 99%

“…As an effective stochastic optimization method, evolutionary computation (EC) is inspired by the evolutionary process of nature [22] and uses the principle of natural evolution to find an optimal solution [23]. EC has been applied to various combinatorial optimization problems, such as traveling salesman problem [24], [25], data mining [10], [26]- [29], job shop scheduling problem [30], [31], unit commitment problems [32], [33], disassembly sequence planning [34], [35], feature selection [36], [37], etc. To explore the huge search space of HUIM, EC-based approaches, e.g., genetic algorithms (GA) [20], [29], particle swarm optimization (PSO) [10], [28], [29], ant colony optimization (ACO) [38], and the artificial bee colony algorithm [39], have been introduced.…”

Section: Introductionmentioning

confidence: 99%

Improved Genetic Algorithm for High-Utility Itemset Mining

et al. 2019

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High-utility itemset mining (HUIM) is an important research topic in the data mining field. Typically, traditional HUIM algorithms must handle the exponential problem of huge search space when the database size or number of distinct items is very large. As an alternative and effective approach, evolutionary computation (EC)-based algorithms have been proposed to solve HUIM problems because they can obtain a set of nearly optimal solutions in limited time. However, it is still time-consuming for EC-based algorithms to find complete high-utility itemsets (HUIs) in transactional databases. To address this problem, we propose an HUIM algorithm based on an improved genetic algorithm (HUIM-IGA). In addition, a neighborhood exploration strategy is proposed to improve search efficiency for HUIs. To reduce missing HUIs, a population diversity maintenance strategy is employed in the proposed HUIM-IGA. An individual repair method is also introduced to reduce invalid combinations for discovering HUIs. In addition, an elite strategy is employed to prevent the loss of HUIs. Experimental results obtained on a set of real-world datasets demonstrate that the proposed algorithm can find complete HUIs in terms of the given minimum utility threshold, and the timeconsuming of HUIM-IGA is relatively lower when mining the same number of HUIs than state-of-the-art EC-based HUIM algorithms.INDEX TERMS Data mining, high-utility itemset mining, genetic algorithm, neighborhood exploration, diversity maintenance. of the Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangsu. His major research areas and work are related to computational intelligence, machine learning, bioinformatics, and among others. He published more than 150 articles in journals, conference proceedings, and several books in the above areas.

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“…Through these financial support, the following articles have been published [31][32][33][34][35][36][37][38][39].…”

Section: Acknowledgementsmentioning

confidence: 99%