High utility itemset mining: a Boolean operators-based modified grey wolf optimization algorithm

Pazhaniraja, N.; Sountharrajan, S.; Kumar, B. Sathis

doi:10.1007/s00500-020-05123-z

Cited by 20 publications

(7 citation statements)

References 33 publications

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“…14 The modified binary version of gray wolf optimization (GWO) algorithm called Boolean GWO is being used to solve HUIM by five different Boolean operations: De Morgan's AND, binary adder, difference and uncertainty multiplexer. 15 A threshold of too large or too short value can result in improper high utility element sets, this makes the task of deciding the exact utility threshold a harder one in HUIM. In Reference 16, employed a methodology based on binary particle swarm optimization to maximize the search for high value items without first establishing a minimum utility criterion.…”

Section: High Utility Itemset Mining (Huim)mentioning

confidence: 99%

“…A dataset of different advertising ads shown in one site is considered in order to extract the High Utility Itemset and PSO‐algorithms to mine ads with a converting rate higher than the converting rate threshold, which are used to predict types of ads leading to the generation of the higher revenue transaction data 14 . The modified binary version of gray wolf optimization (GWO) algorithm called Boolean GWO is being used to solve HUIM by five different Boolean operations: De Morgan's AND, binary adder, difference and uncertainty multiplexer 15 . A threshold of too large or too short value can result in improper high utility element sets, this makes the task of deciding the exact utility threshold a harder one in HUIM.…”

Section: Related Workmentioning

confidence: 99%

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Retracted: High utility itemset mining using genetic algorithm assimilated with off policy reinforcement learning to adaptively calibrate crossover operation

Logeswaran¹,

Suresh²

2021

Computational Intelligence

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Mining high utility itemsets (HUI) is a current thrust field in data mining that has received numerous methodologies for addressing it effectively. The difficulty with HUI is to locate a number of items that have a high degree of utility in comparison to other different sets in a transaction database. Traditional accurate HUIM algorithms usually have to solve the exponential problem of big search spaces when the size or number of different items in the database is quite vast.

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Section: High Utility Itemset Mining (Huim)mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Retracted: High utility itemset mining using genetic algorithm assimilated with off policy reinforcement learning to adaptively calibrate crossover operation

Logeswaran¹,

Suresh²

2021

Computational Intelligence

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“…Unlike the HUIM algorithms described above, this paper designs a particle flter-based HUIM method that uses sampling to flter out HUIs in a dataset. Among the numerous research studies on HUIM algorithms, the heuristicbased HUIM algorithms [26][27][28][29][30][31][32][33][34][35][36][37][38] are the most relevant to us. Inspired by the heuristic methods [39,40], heuristic-based HUIM algorithms frst generate random initial candidates, then update the candidates using behavioral patterns of natural organisms, and fnally, flter out the HUIs from the candidates.…”

Section: Related Workmentioning

confidence: 99%

“…TKU-CE+ [34] then further improves the accuracy of TKU-CE by ignoring low-utility candidates and randomly generating new itemsets. In addition, HUIM-ABC [35], HUIM-ACS [36], and GWO [37] are all heuristic-based algorithms. Tey discover HUIs from candidates.…”

Section: Related Workmentioning

confidence: 99%

A High Utility Itemset Mining Algorithm Based on Particle Filter

Yang

Ding

Wang

et al. 2023

Mathematical Problems in Engineering

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High utility itemset mining is an interesting research in the field of data mining, which can find more valuable information than frequent itemset mining. Several high-utility itemset mining approaches have already been proposed; however, they have high computational costs and low efficiency. To solve this problem, a high-utility itemset mining algorithm based on the particle filter is proposed. This approach first initializes a population, which consists of particle sets. Then, to update the particle sets and their weights, a novel state transition model is suggested. Finally, the approach alleviates the particle degradation problem by resampling. Substantial experiments on the UCI datasets show that the proposed algorithm outperforms the other previous algorithms in terms of efficiency, the number of high-utility itemsets, and convergence.

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“…An optimization model called grey wolf optimization algorithm, which dubs the behaviour of grey wolf and applied to unravel the HUI mining problems by utilising five different Boolean operations [16]. The Boolean operators are used to convert continuous GWO to modified GWO.…”

Section: Review Of Literaturementioning

confidence: 99%

Particle Swarm Optimization Method Combined with off Policy Reinforcement Learning Algorithm for the Discovery of High Utility Itemset

Logeswaran¹,

Suresh

Anandamurugan³

2023

ITC

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Mining of High Utility Itemset (HUI) is an area of high importance in data mining that involves numerous methodologies for addressing it effectively. When the diversity of items and size of an item is quite vast in the given dataset, then the problem search space that needs to be solved by conventional exact approaches to High Utility Itemset Mining (HUIM) also increases in terms of exponential. This factual issue has made the researchers to choose alternate yet efficient approaches based on Evolutionary Computation (EC) to solve the HUIM problem. Particle Swarm Optimization (PSO) is an EC-based approach that has drawn the attention of many researchers to unravel different NP-Hard problems in real-time. Variants of PSO techniques have been established in recent years to increase the efficiency of the HUIs mining process. In PSO, the Minimization of execution time and generation of reasonable decent solutions were greatly influenced by the PSO control parameters namely Acceleration Coefficient and and Inertia Weight. The proposed approach is called Adaptive Particle Swarm Optimization using Reinforcement Learning with Off Policy (APSO-RLOFF), which employs the Reinforcement Learning (RL) concept to achieve the adaptive online calibration of PSO control and, in turn, to increase the performance of PSO. The state-of-the-art RL approach called the Q-Learning algorithm is employed in the APSO-RLOFF approach. In RL, state-action utility values are estimated during each episode using Q-Learning. Extensive tests are carried out on four benchmark datasets to evaluate the performance of the suggested technique. An exact approach called HUP-Miner and three EC-based approaches, namely HUPEUMU-GRAM, HUIM-BPSO, and AGA_RLOFF, are used to relate the performance of the anticipated approach. From the outcome, it is inferred that the performance metrics of APSO-RLOFF, namely no of discovered HUIs and execution time, outstrip the previously considered EC computations.

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High utility itemset mining: a Boolean operators-based modified grey wolf optimization algorithm

Cited by 20 publications

References 33 publications

Retracted: High utility itemset mining using genetic algorithm assimilated with off policy reinforcement learning to adaptively calibrate crossover operation

Retracted: High utility itemset mining using genetic algorithm assimilated with off policy reinforcement learning to adaptively calibrate crossover operation

A High Utility Itemset Mining Algorithm Based on Particle Filter

Particle Swarm Optimization Method Combined with off Policy Reinforcement Learning Algorithm for the Discovery of High Utility Itemset

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