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Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019) pp 1281–1289Cite as

Novel Method of Knowledge Database Data Mining by Association Rules Extraction Technology in Decision Tree

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1088))

Abstract

The extraction method of association rules is to find frequent itemset pattern knowledge from a given dataset. In the decision tree method, the example set is regarded as a discrete information system, and its information is represented by information entropy. In this paper, the theory and algorithm of association rules in data mining technology and decision tree are systematically studied, the theoretical model is established, the corresponding association rules mining algorithms are designed, and the simulation experiments of these algorithms are carried out. The paper presents novel method of knowledge database data mining by association rules extraction technology in decision Tree.

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Author information

Authors and Affiliations

  1. Luoyang Normal University, Luoyang, 471934, China

    Xiuying Zhao & Xiangkui Chen

Authors
  1. Xiuying Zhao
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  2. Xiangkui Chen
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Corresponding author

Correspondence to Xiuying Zhao .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Chuanchao Huang

  2. Computer Science and Information Management, Providence University, Taichung, Taiwan

    Yu-Wei Chan

  3. University of Aizu, Fukushima, Japan

    Neil Yen

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© 2020 Springer Nature Singapore Pte Ltd.

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Cite this paper

Zhao, X., Chen, X. (2020). Novel Method of Knowledge Database Data Mining by Association Rules Extraction Technology in Decision Tree. In: Huang, C., Chan, YW., Yen, N. (eds) Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019). Advances in Intelligent Systems and Computing, vol 1088. Springer, Singapore. https://doi.org/10.1007/978-981-15-1468-5_150

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