An intelligent feature selection approach with systolic tree structures for efficient association rules in big data environment

Abushark, Yoosef B.

doi:10.1016/j.compeleceng.2022.108080

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…In the development of the association rule method, another analysis is required to justify the performance of the new model compared to the traditional approach. Meanwhile, the evaluation needs to consider the runtime and memory usage when running the model [8], [11], [15], [17], [38], [39]. This is because the model with less runtime and memory usage has better performance.…”

Section: B Evaluation Of Association Rule Miningmentioning

confidence: 99%

“…A popular and widely used data mining task is association rule mining [11]- [13]. This task identifies association relationships with items in transactions to be used as a basis for determining promotional bundling, recommendation systems, and item layouts in stores [5], [7], [8], [10]- [17] [18].…”

Section: Introductionmentioning

confidence: 99%

“…When these approaches have been implemented in the recommender system, the MSE or RMSE is also applied. However, the performance of the association rule method is analyzed using the runtime and memory usage during the model processing [11], [17]. This approach has also been widely used in previous research [8], [11], [15], [38], [39] to compare several methods…”

Section: Introductionmentioning

confidence: 99%

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Pruning Strategy on Adaptive Rule Model by Sorting Utility Items

2022

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The adaptive Rule Model is an association rule development that formulates a minimum threshold value according to the data characteristics. The formulation process is based on item frequency and utility for other considerations, which requires high runtime and memory usage. For better performance, it is necessary to develop a pruning strategy that can minimize runtime and memory usage without reducing the quality of the rule produced. Therefore, this research aims to develop 2 pruning strategies, namely the Adaptive Rule with Average Utility (ADR-AU) and the Adaptive Rule with Sorting Utility (ADR-SU) to improve the adaptive rule model. The ADR-AU changes the minimum threshold using the average utility item. The ADR-SU model proposes an additional step in the frequent itemset determination process. It is used for sorting utility items in descending order before the iteration process is carried out to compare whether the values are greater than the minimum threshold. Based on the experiments on 8 datasets, the number of frequent itemset in the ADR-AU and ADR-SU models was significantly less in the adaptive rule model. The results showed that all datasets experienced a decrease in runtime between the adaptive rule model and ADR-AU. Meanwhile, for the Zoo and Sco datasets, there was an increase in runtime between the ADR-AU and ADR-SU models. In terms of memory usage, the connect and retail datasets experienced a decrease, while the other 4 were increased at the ADR-AU and ADR-SU stages.

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Section: B Evaluation Of Association Rule Miningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pruning Strategy on Adaptive Rule Model by Sorting Utility Items

2022

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“…Its main function is to reduce computational complexity, avoid the "curse of dimensionality" problem [1], reduce training time, and improve the performance of the predictor [2]. Therefore, how to effectively extract system features is one of the key issues in the field of time series analysis [3], which has been widely used in the following fields: image recognition [4,5], natural language processing [6,7], data mining [8,9], fault diagnosis [10][11][12], remaining useful life prediction [13,14], microbes classification [15], fatigue detection [16], image classification [17], intrusion detection [18][19][20][21], etc.…”

Section: Introductionmentioning

confidence: 99%

Time Series Feature Selection Method Based on Mutual Information

Huang,

Zhou,

Shi

et al. 2024

Applied Sciences

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Time series data have characteristics such as high dimensionality, excessive noise, data imbalance, etc. In the data preprocessing process, feature selection plays an important role in the quantitative analysis of multidimensional time series data. Aiming at the problem of feature selection of multidimensional time series data, a feature selection method for time series based on mutual information (MI) is proposed. One of the difficulties of traditional MI methods is in searching for a suitable target variable. To address this issue, the main innovation of this paper is the hybridization of principal component analysis (PCA) and kernel regression (KR) methods based on MI. Firstly, based on historical operational data, quantifiable system operability is constructed using PCA and KR. The next step is to use the constructed system operability as the target variable for MI analysis to extract the most useful features for the system data analysis. In order to verify the effectiveness of the method, an experiment is conducted on the CMAPSS engine dataset, and the effectiveness of condition recognition is tested based on the extracted features. The results indicate that the proposed method can effectively achieve feature extraction of high-dimensional monitoring data.

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Weighted bidirectional gated recurrent network for event detection

Mary Vidya,

Ramakrishna

2024

Knowl Inf Syst

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An intelligent feature selection approach with systolic tree structures for efficient association rules in big data environment

Cited by 4 publications

References 14 publications

Pruning Strategy on Adaptive Rule Model by Sorting Utility Items

Pruning Strategy on Adaptive Rule Model by Sorting Utility Items

Time Series Feature Selection Method Based on Mutual Information

Weighted bidirectional gated recurrent network for event detection

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