摘要
在当前的电子证据数据挖掘任务中,数据挖掘算法的性能不够理想,使得电子取证的精度和效率受到影响。为此,研究提出一种优化的Apriori算法。首先,利用Simhash算法提取数据特征并匹配数据;针对K-means算法的缺陷,利用狮群优化算法(Lion Swarm Optimization algorithm,LSO)对其进行优化;针对Apriori算法的缺陷,提出相应的改进策略。结合上述内容,构建SLK-Apriori算法,并将其应用到电子证据数据挖掘中。结果显示,SLK-Apriori算法的损失值为0.11,F1值为95.04%,准确率为98.97%,AUC值达到0.984。综上所述,研究提出的SLK-Apriori算法能够有效地对电子证据数据进行数据挖掘,并且有着十分优异的性能,能够提升数据挖掘效果和效率,对我国公安系统的信息化建设有正面意义。
In current electronic evidence data mining tasks,the performance of data mining algorithms is not ideal,which affects the accuracy and efficiency of electronic forensics.Therefore,an optimized Apriori algorithm is proposed.Firstly,Simhash algorithm is used to extract data features and match data;Aiming at the defects of K-means algorithm,Lion Swarm Optimization Algorithm(LSO) is used to optimize it;Aiming at the defects of Apriori algorithm,corresponding improvement strategies are proposed.Combining the above content,the SLK-Apriori algorithm is constructed and applied to electronic evidence data mining.The results show that the loss value of the SLK-Apriori algorithm is 0.11,the F1 value is 95.04%,the accuracy rate is 98.97%,and the AUC value reaches 0.984.In summary,the SLK-Apriori algorithm proposed in the study can effectively mine electronic evidence data,and has excellent performance.It can improve the effectiveness and efficiency of data mining,and has positive significance for the informatization construction of China's public security system.
作者
王刚
WANG Gang(Shaanxi Police College,Xi’an 710021,China;Shaanxi Smart Criminal Technology Application Research Center,Xi’an 710021,China)
出处
《自动化与仪器仪表》
2023年第12期47-50,55,共5页
Automation & Instrumentation
基金
陕西省智慧新刑技实战应用研究中心自主基金课题(SXZHXJ202201)。
