Application of improved time series apriori algorithm in data mining of association rules based on temporal constraint
The basic idea of Apriori algorithm is to nd all the frequent sets in the transaction, and the frequent need of these frequent sets is greater than or equal to the minimum support degree of the set. Then the paper describes the working principle of the traditional Apriori algorithm, and points out the existing problems. To solve these problems, this paper proposes an improved Apriori algorithm for frequent item set time series. In this paper, we analyze the methods and procedures of time series and time sequence association rules mining. The paper presents combined application of improved time series Apriori algorithm by frequent itemsets in association rule data mining based on temporal constraint. The last experiment shows that the improved Apriori algorithm is better than the traditional method in the storage space.