Application of Improved K-Means Clustering Algorithm in Customer Segmentation

Abstract

Market competition is the competition for customers. By adopting customer segmentation model, decision makers can effectively identify valuable customers and then develop effective marketing strategy. Cluster analysis is one of the major data analysis methods and the k-means clustering algorithm is widely used. But the original k-means algorithm is computationally expensive and the quality of the resulting clusters heavily depends on the selection of initial centroids. An improved K-means algorithm is presented,with which K value of clustering number is located according to the clustering objects distribution density of regional space,and it uses centroids of high-density region as initial clustering center points. The proposed method makes the algorithm more effective and efficient, so as to gets better clustering with reduced complexity.