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Association rules redundancy processing algorithm based on hypergraph in data mining

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Abstract

In order to achieve the research from individual data to data system and from passive verification of data to active discovery, taking high dimensional data oriented data mining technology as the research object, an association rule redundancy processing algorithm based on hypergraph in data mining technology is studied according to the project requirements. The concepts of hypergraph and system are introduced to explore the construction of hypergraph on 3D matrix model. In view of the characteristics of big data, a new method of super edge definition is adopted, which improves the ability of dealing with problems. In the association rules redundancy and loop detection based on directed hypergraph, the association rules are transformed into directed hypergraph, and the adjacency matrix is redefined. The detection of redundancy and loop is transformed into the processing of connected blocks and circles in hypergraph, which provides a new idea and method for the redundant processing of association rules. The new method is applied to the data processing of practical projects. The experimental results show that the 3D matrix mathematical model and related data mining algorithms in this paper can find new high-quality knowledge from high-dimensional data.

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Acknowledgements

This work was supported by The National Natural Science Foundation of China (Grant Nos. 71001075 and 61471090), and the Fundamental Research Funds for the Central Universities (Grant No. skqy201739).

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Authors and Affiliations

  1. Business School of Sichuan University, Chengdu, China

    Maozhu Jin & Hua Wang

  2. Economic and Management School, Chengdu Agricultural College, Chengdu, 610064, China

    Qian Zhang

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  1. Maozhu Jin
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  2. Hua Wang
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  3. Qian Zhang
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Correspondence to Qian Zhang.

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Jin, M., Wang, H. & Zhang, Q. Association rules redundancy processing algorithm based on hypergraph in data mining. Cluster Comput 22 (Suppl 4), 8089–8098 (2019). https://doi.org/10.1007/s10586-017-1632-7

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  • DOI: https://doi.org/10.1007/s10586-017-1632-7

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