Abstract: Clustering customers’ transaction data is an important analysis means of customers’ behavior.As customers’ transaction data have high dimension，the clustering algorithm based on Empirical Mode Decomposition（EMD） and K-means is implemented to cluster the customers’ actions in supermarkets，that is，employ the EMD and bottom-up algorithms to realize dimension reduction，and further use K-means algorithm to support effective clustering on data sequences，which have fewer dimensions. Customers are divided into different categories（or sub-market） by means of clustering customers’ transaction data.Each sub-market is then described by the commodities which are purchased with higher rates，so as to make respective promotions and advertisements.The clustering algorithm proposed in this paper can effectively cluster customers’ behavior，and as the algorithm has dealt with dimensionality reduction to a sequence of transaction data，this can save a certain amount of storage space.