On a Machine Learning Based Method for Store Clustering

Since the consuming behavior of customers is hard to predict and most data is diversified and unclassified in the real world, it is of great importance to collect those data and analyze them so as to improve the promotion strategy and total profit for business activity. To solve this problem, an unsupervised model in machine learning is utilized to analyze the transaction dataset and then classify the stores into multiple groups. A particular example dataset of a shopping mall is represented to demonstrate that the proposed model is correct and effective.