Analysis of Consumption Behavior Characteristics of Business Users Based on Dissimilarity Function Improved K-Means Clustering Algorithm

Abstract

Aiming at the low efficiency and accuracy of K-means algorithm in processing massive data, an improved K-means clustering algorithm based on dissimilarity function was proposed. The Euclidean distance internal weighted method was used to improve the traditional distance algorithm, and a new dissimilarity function was constructed to calculate the distance of the cluster center. Experimental results showed that compared with the traditional K-means clustering algorithm, the improved K-means clustering algorithm has a faster convergence speed and higher accuracy in the algorithm verification. In practical applications, after cluster analysis is performed on the proportion of page access times, more accurate user consumption behavior characteristics are obtained. Therefore, based on the improved K-means clustering algorithm, the consumption behavior characteristics of business users can be described and analyzed well.