基于改进关联规则算法的警情数据分析  

Analysis of Alarm Data Based on Improved Association Rules Algorithm

作　　者：王云 李丛 WANG Yun;LI Cong(Mobile Internet College,Taizhou Institute of Science and Technology,NJUST,Taizhou 225300,China)

机构地区：[1]南京理工大学泰州科技学院移动互联网学院

出　　处：《计算机与现代化》2019年第12期1-5,共5页Computer and Modernization

基　　金：国家自然科学基金资助项目(61321491)

摘　　要：针对传统Apriori算法挖掘警情数据的缺点,提出一种改进的Apriori算法。该算法首先在关联规则发现阶段引入权值参数,设计支持度阈值函数,以挖掘不常发生的重大案情发生规律;然后提出一种压缩矩阵优化算法,将数据压缩存储在只有0或1的矩阵中,并用2个数组来记录矩阵中每一行及每一列1的总数,可多次压缩矩阵,提升挖掘效率;最后将改进的算法用于实际的警情数据挖掘分析,给出关联规则挖掘结果。实验表明,改进算法不仅执行效率较传统算法有所提升,且针对警情数据的挖掘结果准确性也有所提高。Aiming at the shortcomings of traditional Apriori algorithm for mining alarm data,an improved Apriori algorithm is proposed.Firstly,the algorithm introduces the weight parameter in the association rule discovery stage,and designs the support degree threshold function to mine the abnormal case occurrence law.Then a compression matrix optimization algorithm is proposed to store the compressed data in only 0 or 1.In the matrix,two arrays are used to record the total number of 1 for each row and each column in the matrix.The matrix can be compressed multiple times to improve the mining efficiency.Finally,the improved algorithm is applied to the actual police data mining analysis,and the association rules are given from mining results.Experiments show that the improved algorithm not only improves the execution efficiency compared with the traditional algorithm,but also improves the accuracy of the mining results for the police data.

关 键 词：警情数据 关联规则 APRIORI算法 压缩矩阵 权值参数 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]