An improved Apriori algorithm based on influence weight

Abstract

Aiming at the problems in the actual mining process of the Apriori algorithm: it does not take into account the relationship between the influence of the item, and cannot mine the association rules of some small probability events, frequently scan the database and generate a large number of candidate itemsets, an improved Apriori algorithm based on the influence weight is proposed. Firstly, according to the impact of items on affairs and the relationship between items, a weight distribution method based on item influence is used to weight items, so as to extract more hidden and valuable associated information. Secondly, in order to reduce the scanning times of the database and the overhead of I/O ports, the weight vector and compression matrix are introduced to represent the transaction database. Finally, it is compared with the classical Apriori algorithm and the Apriori algorithm based on compressed matrix. Experimental results show that the improved Apriori algorithm has higher efficiency, and can mine out frequent patterns with high influence.