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Tytuł:
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Research on Embedded Multifunctional Data Mining Technology Based on Granular Computing.
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Autorzy:
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Li J; School of Computer Engineering, Jinling Institute of Technology, Nanjing, Jiangsu 211169, China.; Jiangsu Provincial Key Laboratory of Data Science and Intelligent Software, Nanjing, Jiangsu 211169, China.
Tian X; School of Computer Engineering, Jinling Institute of Technology, Nanjing, Jiangsu 211169, China.; Jiangsu Provincial Key Laboratory of Data Science and Intelligent Software, Nanjing, Jiangsu 211169, China.
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Źródło:
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Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Jun 20; Vol. 2022, pp. 4825079. Date of Electronic Publication: 2022 Jun 20 (Print Publication: 2022).
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: New York, NY : Hindawi Pub. Corp.
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MeSH Terms:
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Data Mining*/methods
Neural Networks, Computer*
Technology
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Entry Date(s):
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Date Created: 20220630 Date Completed: 20220701 Latest Revision: 20220701
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Update Code:
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20240105
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PubMed Central ID:
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PMC9236840
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DOI:
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10.1155/2022/4825079
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PMID:
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35769268
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Due to the influence and limitations of the multisourced, heterogeneous, and unbalanced characteristics of embedded multifunctional data, the application effect of the current data mining technology is not good, and the accuracy is low. To solve the above problems, an embedded multifunctional data mining technology based on granular computing was studied. According to the three characteristics of embedded multifunctional data, preprocessing such as data reduction, data standardization, and data balance were implemented. We implemented data granulation for the preprocessed data and calculated the data granulation characteristics, including offset, particle density, and intraparticle interval. Taking granular features as the input content, embedded multifunctional data mining was realized by using a neural network to complete the objectives of data classification, anomaly detection, fault identification, and so on. The experimental results showed that the anomaly mining results of each type of data mining were greater than 0.9, indicating that the accuracy of the mining technology is high.
Competing Interests: The authors declare that they have no conflicts of interest.
(Copyright © 2022 Juan Li and Xianghong Tian.)
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