Heuristically mining the top-k high-utility itemsets with cross-entropy optimization

Song, Wei; Zheng, Chuanlong; Huang, Chao‐Min; Liu, Lu

doi:10.1007/s10489-021-02576-z

Cited by 15 publications

(4 citation statements)

References 38 publications

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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%

“…HUIM-SA [33] then adds an acceptance probability that determines whether the current HUIs are used as target values to avoid the algorithm falling into a locally optimal solution. TKU-CE [34] iteratively fnds the top-k HUIs with higher utility values using a probability vector. TKU-CE+ [34] then further improves the accuracy of TKU-CE by ignoring low-utility candidates and randomly generating new itemsets.…”

Section: Related Workmentioning

confidence: 99%

“…TKU-CE [34] iteratively fnds the top-k HUIs with higher utility values using a probability vector. 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.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A High Utility Itemset Mining Algorithm Based on Particle Filter

Yang

Ding

Wang

et al. 2023

Mathematical Problems in Engineering

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“…Therefore, optimization is made. Equation ( 17 ) 20 , 21 signifies the situation in a regret-joy function where decision makers choose scheme other than ideal scheme under the attribute of state . …”

Section: Research On Utility Risk Entropy Algorithm Based On Clusteri...mentioning

confidence: 99%

Decision optimization of emergency material support based on blockchain under major public health emergencies

Wang

Fan

KunBao

2022

Sci Rep

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The work intends to relieve the pressure on the urban medical system and reduce the cross-infection of personnel in major public health emergencies. On the premise of an in-depth analysis of the utility risk entropy algorithm model and prospect theory, the decision-making of major health emergencies is proposed. Firstly, the utility risk entropy algorithm model is optimized, and the main decision-making members are subjected to utility perception according to the perceived utility values of different levels of risk, and the weights of decision-making members are calculated and revised according to the results of utility clustering. Secondly, the prospect theory is optimized. Taking the zero as the reference point to calculate the prospect value, and taking the maximization of the comprehensive prospect value as the objective to optimize the model, the comprehensive prospect value of each scheme is calculated and sorted. Finally, the proposed scheme is tested, and the test results show that in the optimal decision-making time of the scheme, the optimal decision-making time is 0 every day. When the epidemic situation is in the first cycle, the decision-making loss of the optimal scheme is 2.69, and the reduction ratio of the optimal scheme decision-making loss is 63.96%. When the epidemic situation is in the second cycle, the decision-making loss of the optimal scheme is 0.65, and the reduction ratio of the optimal scheme decision-making loss is 94.44%. When the epidemic situation is in the third cycle, the decision-making loss of the optimal scheme is 0.22, and the reduction ratio of the decision-making loss of the optimal scheme is 89.39%. The proposed scheme can improve the processing efficiency of major health emergencies and reduce the risk of accidents.

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SFU-CE: Skyline Frequent-Utility Itemset Discovery Using the Cross-Entropy Method

Song¹,

Zheng²

2021

Intelligent Data Engineering and Automated Learning – IDEAL 2021

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Heuristically mining the top-k high-utility itemsets with cross-entropy optimization

Cited by 15 publications

References 38 publications

A High Utility Itemset Mining Algorithm Based on Particle Filter

A High Utility Itemset Mining Algorithm Based on Particle Filter

Decision optimization of emergency material support based on blockchain under major public health emergencies

SFU-CE: Skyline Frequent-Utility Itemset Discovery Using the Cross-Entropy Method

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