A novel hybrid GA–PSO framework for mining quantitative association rules

Moslehi, Fateme; Haeri, Abdorrahman; Martínez‐Álvarez, Francisco

doi:10.1007/s00500-019-04226-6

Cited by 45 publications

(20 citation statements)

References 48 publications

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“…The main idea of deep page Association prefetch is to not only consider the current request of customers, but also consider the current request sequence of customers or the suffix sub-sequence of the current request sequence of customers, so as to improve the accuracy of prefetch [21][22][23]. The basic idea of prefetch model is to use the historical access sequence of customers to build a deep structure tree.…”

Section: Deep Page Association Prefetch Modelmentioning

confidence: 99%

Design and Analysis of Low Delay Deterministic Network Based on Data Mining Association Analysis

Gong

2021

JWE

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The purpose of this paper is to research on the design and analysis of low delay deterministic network based on data mining association. This paper studies and implements the algorithm of mining page association rules. A session recognition algorithm based on log reference page and request time is proposed by using the time probability relationship of continuous requests. This method improves the accuracy of log data preprocessing and page association rules mining. This paper studies and tests the efficiency, prefetch timing and cache organization of the two page association rules. The results show that in this prefetch scheme, the prefetch performance of the association rules of unordered pages is better than that of the association rules of ordered pages. The prefetch performance when cache hits is better than that when cache fails and the cache fails to hit has better performance. In the case of a certain size of cache space, reasonable organization of cache space can further improve the cache hit rate and reduce the network delay.

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Section: Deep Page Association Prefetch Modelmentioning

confidence: 99%

Design and Analysis of Low Delay Deterministic Network Based on Data Mining Association Analysis

Gong

2021

JWE

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“…Modeling with an Artificial Neural Network Abiodun et al has reported the details of feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance [19]. Additionally; research has also been done on hybrid GA -PSO framework modeling in data mining [20]. According to literature researches, it is understood that ANN model is frequently used in different disciplines by processing non-linear data among many artificial learning rules and the results are a more common alternative to methods such as Fuzzy Control [21].…”

Section: Radiography and Metallographic Examinationmentioning

confidence: 99%

Prediction of shrinkage ratio of ZA 27 die casting alloy with artificial neural network, computer aided simulation and comparison with experimental studies

Kumruoğlu

2021

Scientia Iranica

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In the cast alloys with a long freezing range such as ZA-27, casting defects such as porosity and shrinkage will occur if casting variables are not controlled carefully. In this study, the effect of casting variables on shrinkage and micro-porosity defects in ZA-27 was investigated. The defects of casting were predicted with Artificial Neural Network algorithms. The cooling rate, solidification time, temperature, liquid phase, initial mold temperature, and %shrinkage were obtained from a series of simulation-experimental tests. The heat transfer coefficient of ZA-27 and graphite die was calculated as 2000 W/(m 2 K). In the samples poured into the mold heated at 350 °C, the minimum feeder shrinkage volume was observed. Locations of the chronic Hotspot and Shrinkage problem was estimated. It was observed that the casting heated to 150 °C had wide-deep shrinkage on the upper and lateral surfaces of the feeder. A good correlation was obtained between the modeling results of the ANN and the experimental results. Optimum ANNs were designed, trained, and tested to predict the shrinkage rate for various initial mold temperatures and physical conditions. Thanks to the sigmoid (sigmoaxon) function training, the most systematic modeling ANN set was revealed with 99% (Vol. 7.65% Shrinkage) prediction.

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“…Therefore, many scholars have explored the application of heuristic algorithms in this field and have made rich achievements (del Jesus et al , 2011). In particular, the improved heuristic algorithms for genetic algorithm, particle swarm algorithm, ant colony algorithm, and other heuristic methods (Al-Dharhani et al ., 2014; Prasanna and Ezhilmaran, 2016; Moslehi et al , 2020) all solve the shortcomings of traditional algorithms in data mining and have been well applied in the fields of education, medicine, services (Wang et al ., 2019 b ; Fang et al , 2020; Shazad et al , 2020), which fully verifies the superiority of this heuristic method. The principles and features of these algorithms are shown in Table 1.…”

Section: Related Workmentioning

confidence: 99%

Association rules mining between service demands and remanufacturing services

Zhou

Xia

Zhang

et al. 2020

AIEDAM

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The potential relationship between service demands and remanufacturing services (RMS) is essential to make the decision of a RMS plan accurately and improve the efficiency and benefit. In the traditional association rule mining methods, a large number of candidate sets affect the mining efficiency, and the results are not easy for customers to understand. Therefore, a mining method based on binary particle swarm optimization ant colony algorithm to discover service demands and remanufacture services association rules is proposed. This method preprocesses the RMS records, converts them into a binary matrix, and uses the improved ant colony algorithm to mine the maximum frequent itemset. Because the particle swarm algorithm determines the initial pheromone concentration of the ant colony, it avoids the blindness of the ant colony, effectively enhances the searchability of the algorithm, and makes association rule mining faster and more accurate. Finally, a set of historical RMS record data of straightening machine is used to test the validity and feasibility of this method by extracting valid association rules to guide the design of RMS scheme for straightening machine parts.

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A novel hybrid GA–PSO framework for mining quantitative association rules

Cited by 45 publications

References 48 publications

Design and Analysis of Low Delay Deterministic Network Based on Data Mining Association Analysis

Design and Analysis of Low Delay Deterministic Network Based on Data Mining Association Analysis

Prediction of shrinkage ratio of ZA 27 die casting alloy with artificial neural network, computer aided simulation and comparison with experimental studies

Association rules mining between service demands and remanufacturing services

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