The personal financial market segmentation plays an important role in retail banking. It is widely admitted that there are a lot of limitations of conventional ways in customer segmentation, which are knowledge based and often get bias results. In contrast, data mining can deal with mass of data and never miss any useful knowledge. Due to the mass storage volume of unlabeled transaction data, in this paper, we propose a clustering ensemble method based on majority voting mechanism and two alternative manners to further enhance the performance of customer segmentation in real banking business. Through the experiments and examinations in real business environment, we can come to a conclusion that our model reflect the true characteristics of various types of customers and can be used to find the investment preferences of customers.
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