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Question 2 The amount of data collected by banks has increased rapidly since recent decades, opening up a lot of opportunities for data mining applications. One of the most used data mining techniques in banks is clustering. Regarding the application of clustering in the banking industry, Farajian and Mohammadi (2010, p. 241) made the following comments: "Banks seeking newer and better ways to differentiate themselves from their competitors, customer clustering one of important way to rich this result; Customer clustering is the use of past transaction data to divide customer to the similar groups. The results produced are based on the assumptions that the customer behavior follows patterns similar to past pattern and repeats in the future. Therefore, there could not be a better time than now to analyze the importance of an effective new marketing strategy using the customer behavior analyze. The decisions to be made include which target groups of customers will be encouraged to use more, what terminal type to assign, how estimated probability of acceptance new products, whether to promote new products to target groups of customers, and, how to manage groups of customers to rich the customer satisfaction and direct marketing" (Direct quotes are provided, which are not amended for grammatical and/or typographical errors) Reference: Farajian, M.A. & Mohammadi, S. (2010), "Mining the Banking Customer Behavior Using Clustering and Association Rules Methods", International Journal of Industrial Engineering and Production Research, Vol. 21, No. 4, pp. 239-245. (a) With reference to the CRISP-DM framework, discuss how a bank can plan a data mining project in which clustering is applied to mine customer behaviour. In your answer, you can pick any area (e.g. marketing, loan approval, fraud detection, customer acquisition and retention) in which clustering can be applied in the banking industry for your discussion. Your answer should be less than 500 words. (20 marks) (b) Based on the application proposed in (a), the bank is planning to perform the analytics on the cloud in response to the increasing amount of data. Which deployment model (i.e., private cloud, public cloud or hybrid cloud) will you recommend for the bank? Explain briefly. Your answer should be less than 250 words. (10 marks)