Abstract: 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.

Key words: alarm data, association rules, Apriori algorithm, compression matrix, weight parameter