earticle

영어

As one of the most popular data mining techniques, clustering is an important way of exploratory data analysis and pattern discovery given the explosive increasing amount of dataset. Applying clustering in customer segmentation is a common method to discover high-value customers. However, traditional clustering methods such as k-means are performing single direction (either row or column) on the data matrix, and thus the clustering results might involve cases that are irrelevant to specific dimensions. Besides, traditional clustering is achieved upon the whole set of attributes or variables, and therefore only capable of discovering global information. Along this line, in order to reduce the dimensions and find out potential local patterns in the data matrix, we proposed a bi-clustering algorithm for customer segmentation. Our experiments using supermarket customer dataset improve the effectiveness and efficiency of proposed bi-clustering algorithm.