Improvement of Association Algorithm Based on Matrix Optimization

Authors

  • Yang Chen

DOI:

https://doi.org/10.54097/fcis.v2i3.5411

Keywords:

Frequent itemsets, Boolean matrix, Simplified matrix, Optimal connection

Abstract

In order to solve the problems of high memory usage and low efficiency of frequent itemsets generation in the process of data mining, the existing association algorithm is improved. The data information is expressed in the form of a Boolean matrix, and the matrix is continuously simplified in the operation to optimize the item set connection method, thereby reducing memory consumption and improving algorithm efficiency.

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References

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Ian H. Witten, Eibe Frank and Mark A. Hall. Data Mining: Practical Machine Learning Tools and Techniques [M]. China Machine Press. 2014.

Jiawei Han, Micheline Kamber, Jian Pei, Data Mining Concepts and Techniques Third Edition [M]. China Machine Press. 2019.

Cui Yan, Bao Zhiqiang. A survey of association rule mining[J]. Application Research of Computers, 2016,33(02):330-334.

Cai Weijie, Zhang Xiaohui, Zhu Jianqiu, Zhu Yangyong. A Survey of Association Rules Mining [J]. computer engineering ,2001(05): 31- 33+ 49.

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Published

22-02-2023

Issue

Section

Articles

How to Cite

Chen, Y. (2023). Improvement of Association Algorithm Based on Matrix Optimization. Frontiers in Computing and Intelligent Systems, 2(3), 97-100. https://doi.org/10.54097/fcis.v2i3.5411