Using Clustering Analysis and Association Rule Technology in Cross-Marketing

Abstract

In this paper, according to the perspective of customers and products, by using clustering analysis and association rule technology, this paper proposes a cross-marketing model based on an improved sequential pattern mining algorithm, where an improved algorithm AP (Apriori all PrefixSpan) is applied. The algorithm can reduce the time cost of constructing a projection database and the influence of the increase of support on the algorithm efficiency. The improved idea is that when the first partition is used to generate the projection database, the number of itemsets in the projection database is sorted from small to large, and when the second partition is used, the sequence patterns are generated directly from the mined sequence patterns, so as to reduce the construction of the database. The experimental results show that this method can quickly mine the effective information in complex data sets, improve the accuracy and efficiency of data mining, and occupy less memory consumption, which has good theoretical value and application value.