Association analysis method of drug-related cases based on Apriori and FP-growth algorithm

Yue Wang; Zhiwen Hou; Fanliang Bu; Jinghua Yan

doi:10.1117/12.2579242

10 November 2020 Association analysis method of drug-related cases based on Apriori and FP-growth algorithm

Yue Wang, Zhiwen Hou, Fanliang Bu, Jinghua Yan

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 1158424 (2020) https://doi.org/10.1117/12.2579242
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

Abstract

Drug crimes seriously endanger human health and social peace. Strengthening the correlation analysis of the features of drug-related cases and fully mining up the correlation rules from the features of the cases have become the key links for effectively preventing and responding to drug crimes. After performing performance analysis on the two association analysis algorithms, in view of the lack of existing association analysis research and association rule mining methods in drug-related cases, this paper proposes a method for analyzing association rules of drug-related cases, Through the FP-growth algorithm and K-supported expectation method to calculate the weighted frequent items of drug-related personnel, and use the Apriori algorithm to obtain the feature association rules. The results show that the proposed method makes a good trade-off between the amount of data and the minimum support (called minsup), and can effectively mine association rules from the features of drug-related cases. It is of great significance to prevent and control the development of drug-related crimes in time and improve the ability of public security departments to deal with drug-related cases.

Citation Download Citation

Yue Wang, Zhiwen Hou, Fanliang Bu, and Jinghua Yan "Association analysis method of drug-related cases based on Apriori and FP-growth algorithm", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 1158424 (10 November 2020); https://doi.org/10.1117/12.2579242

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KEYWORDS

Analytical research

Mining

Databases

Information security

Computer security

Data conversion

Data mining

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