Association mining-based method for enterprise’s technological  innovation intelligent decision making under big data

Zhang, Qianqian; Geng, Guining; Tu, Qun Zhang

doi:10.15837/ijccc.2023.2.5241

Cited by 2 publications

(3 citation statements)

References 38 publications

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“…In the study of rule mining in data, research is widely distributed internationally [5,6,7]. Meesala et al used feature analysis of social media comments for rule mining on this dataset.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing Power Grid Data Analysis with Fusion Algorithms for Efficient Association Rule Mining in Large-Scale Datasets

Sun

2024

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Against the backdrop of the rapid development of information technology, the total amount of data has exploded, and efficient association rule mining methods for large-scale datasets have been studied. Conventional rule mining algorithms are subject to electrical constraints when working, and their convergence speed and data noise are currently the main problems they face. In order to accelerate the working process of the algorithm, this study introduces a data warehouse into the K-Means algorithm, and connects the time series and voltage interaction functions with the long-and-short-term memory network for efficient information analysis of power grid data, generating fusion algorithms. The study conducted experiments on the Netloss dataset and simultaneously conducted experiments on three models, including long-and-short-term memory networks, to verify the superiority of the fusion algorithm. Under the same experimental voltage, the circuit power flows of the four models were 0.37, 0.64, 0.79, and 0.82A, respectively, indicating that the algorithm effectively controlled the electrical dataset. Its measurement accuracy was the highest among the four models, at 91.7%. The experimental results show that the fusion algorithm proposed in the study has precise control ability in power grid datasets, and can effectively mine association rules on large-scale datasets.

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“…In the study of rule mining in data, research is widely distributed internationally [5,6,7]. Meesala et al used feature analysis of social media comments for rule mining on this dataset.…”

Section: Related Workmentioning

confidence: 99%

“…LSTM can output its hidden layers as feature representations based on historical data. In this process, each time step will correspond to the current index output, and its calculation method is shown in equation (5).…”

Section: Association Algorithm Combining Clustering and Neural Networkmentioning

confidence: 99%

Enhancing Power Grid Data Analysis with Fusion Algorithms for Efficient Association Rule Mining in Large-Scale Datasets

Sun

2024

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…The explosion of big data has led to millions of datasets available in data lakes, covering fields such as social science, life science, and more [1][2][3]. Access to this data is vital for facilitating reproducible research results, aiding scientists in advancing their work, and providing data journalists with easier access to information and its provenance [4][5][6]. However, the vast scale of datasets in data lakes presents new and intriguing challenges for data management research [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Method for Automatically Generating Join Queries Based on Relations-Attributes Distance Matrix over Data Lakes

2023

Teh. vjesn.

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Techniques for identifying joinable or unionable tables in data lakes can yield valuable information for data scientists. However, more than half of their working time is spent familiarizing themselves with the metadata and correlations of datasets. Simplifying the use of information in data lakes is crucial for enhancing their utilization. The existing solution of integrating correlated relations into a single large data table via full disjunction requires integration updating when either data or metadata changes, complicating data maintenance. This paper proposes a method for automatically generating join queries based on the distance matrix of relations and attributes in data lakes. The distance matrix only requires updating when metadata changes, simplifying data maintenance. Experimental results demonstrate that once the distance matrix is generated, the time required to generate the join queries is negligible. Compared to the existing solution, the time cost for executing join queries over correlated tables is nearly identical to that of selection queries over integrated tables. The results of these two queries are also the same, showcasing the effectiveness and efficiency of our method.

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Association mining-based method for enterprise’s technological innovation intelligent decision making under big data

Cited by 2 publications

References 38 publications

Enhancing Power Grid Data Analysis with Fusion Algorithms for Efficient Association Rule Mining in Large-Scale Datasets

Enhancing Power Grid Data Analysis with Fusion Algorithms for Efficient Association Rule Mining in Large-Scale Datasets

A Method for Automatically Generating Join Queries Based on Relations-Attributes Distance Matrix over Data Lakes

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