MapReduce-based data mining algorithm in consumer behavior prediction

Abstract

In the era of complicated information, consumer behavior mining is of great significance to enterprises and markets, however, the current information processing technology is difficult to comprehensively mine and segment consumer data. The study aims to conduct an effective analysis of consumer behavior. And for the shortcomings of the current consumer behavior mining algorithms, the study proposes an improved consumer behavior data mining algorithm based on the map reduce model. After the experimental analysis, the results revealed that the research algorithm has the closest Mahalanobis Distances compared to the two algorithms, fuzzy C-means and density-based spatial clustering of application with noise, indicating that the research algorithm is more effective in clustering. The average clustering accuracy of K-means clustering algorithm (K-means) based on Andersori’s Iris data seto dataset was 93.2%, and the average clustering accuracy of the two datasets Glass and Wine was 94.3% and 93.8%, respectively. The research methodology categorized consumers into three classes based on their transaction frequency and transaction amount. Among the consumers in cluster 1, the total transaction amount was in the range of 0.62–0.82, the transaction frequency was between 0.41 and 0.72, and the number of transactions was between 0.72 and 0.94, which shows that the consumers in this cluster belonged to the group of moderately active and high consumption. The above data indicate that this method, through the collaborative optimization of MapReduce and K-means, has an accuracy rate of over 90% in cross-industry scenarios. It effectively solves the problems of low efficiency, poor accuracy and weak adaptability of traditional algorithms, providing a quantifiable technical solution for the research of consumer behavior.