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.
Highlights
With the continuous development and progress of science, technology, and economy, the worldwide industrial competition is becoming more and more fierce, and the business model, market environment, and competition model have undergone fundamental changes [1]. is change is more obvious in the information service industry
Identifying cross-marketing opportunities from customer analysis is based on the consumption characteristics of existing customers as the basis of forecasting cross-marketing, studying the purchase differences between different customer groups, so as to recommend specific types of commodity combinations
Based on the research of the PrefixSpan algorithm, this paper studies that the cost of the PrefixSpan algorithm mainly lies in the construction of subdatabases and studies the idea of the Apriori algorithm. e Apriori algorithm is efficient in verifying candidate sets
Summary
With the continuous development and progress of science, technology, and economy, the worldwide industrial competition is becoming more and more fierce, and the business model, market environment, and competition model have undergone fundamental changes [1]. is change is more obvious in the information service industry. Providing new products and services to the existing customers, namely, cross-marketing, plays an important role in expanding profits. Speaking, considering how to tap potential crossmarketing opportunities can start from two directions: one is from business and the other is from customers [3]. Identifying cross-marketing opportunities from customer analysis is based on the consumption characteristics of existing customers as the basis of forecasting cross-marketing, studying the purchase differences between different customer groups, so as to recommend specific types of commodity combinations. To identify cross-marketing opportunities from the perspective of business is to analyse the business characteristics to find out the existing users who meet the characteristics and recommend them [4].
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