# 1 Choosing the right data mining techniques for the job (8 minutes,

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2 the number of entities at that point in the tree. Note that there may be multiple entries with a single company name, which helps to make this approach interesting. Scoring: one point for a method, 1-2 points for a reasonable description of why it is appropriate, 1-2 points for how you would interpret the results. 2 Types of clustering (9 minutes, 6 points) Give an advantage / strong point of each of the following types of clustering. (Your answers can be general - they don t need to be specific to the CRM database above.) K-means clustering Gives a prototype description of the cluster, the entity that would be constructed by taking the cluster mean. Hierarchical clustering Number of clusters can be based on various parameters, including intra-cluster distance, number of clusters. Gives a measure of which clusters are close to each other, which are far apart. Density-based clustering Handles odd-shaped clusters: Items can be distant from each other and still be placed in the same cluster, if appropriate. Scoring: 1 point each for showing evidence that you know what the method is, 1 for a good advantage it has over other methods. 3 Clustering for CRM Marketing is going to run three independent advertising campaigns, each addressing a different segment of current or potential customers. They want you to cluster the companies to help to build these advertising campaigns. 3.1 Choice of Technique (8 minutes, 3 points) What clustering technique/algorithm would you use if your goal was to describe key characteristics of each cluster? Give a brief reason why. I d use k-medoid clustering, with k = 3. This approach handles continuous and discrete attributes (provided I define a distance function), and it would be easy for marketing to understand when I said here is a typical company for this cluster. Scoring: One for a valid clustering technique, 1-2 for good reasons why. 3.2 K-Means (15 minutes, 3 points) Assume you were to cluster the companies using k-means, with k = 3. For just the data you are provided, describe ONE cluster, i.e., list the companies that are in the cluster and calculate the means for that cluster. One cluster consists of MA Micro. Since it is a cluster of a single company (quite distant from any others), the mean would be that entity itself. It isn t meaningful to talk about k-means of categorical attributes, but for the numeric attributes the mean would be 80 employees, since 1995, year 2003, 400 items, \$3500 profit. Scoring: One for giving a reasonable set of companies for a cluster, one for showing evidence you know how to calculate the mean, one for getting a correct mean. 2

3 3.3 Is this the right question? (15 minutes, 2 points) The goal is to maximize revenue. By using clustering of existing customers to develop the advertising campaign, the marketing department may miss something important. Describe something they might miss given the data you have and the data mining technique you suggested, and what else you would need so they wouldn t miss it. This approach will only help us define campaigns for customers similar to those we already have. It wouldn t help to identify new market segments. I d want to include information on potential customers that we don t currently sell to (e.g., our competitors customers.) Scoring: One for something reasonable that it wouldn t provide, one for what you d do about it. 4 Data Preparation In 2002, Compaq and Hewlett-Packard merged (i.e., Hewlett-Packard bought Compaq.) This distorts the data you ll notice that sales to Hewlett-Packard jumped significantly in 2003, not surprisingly to roughly the combined level of the two companies in You could handle this in various ways: Combine the historical records of the two companies, try to split them out in more recent data, or just ignore the change. 4.1 Ignoring the change (8 minutes, 2 points) Give a data mining problem/scenario/technique where it would be appropriate to just take the data as is where the merger wouldn t make a difference. Building a model to predict profit on a new customer from public information. Since this is independent of history, treating Compaq as an independent company would be fine. Scoring: One for a reasonable example, one for explanation. 4.2 Merge the history (8 minutes, 2 points) Give a data mining problem/scenario/technique where it would be appropriate to combine the companys data prior to the merge, e.g., add the sales of Compaq to Hewlett-Packard and eliminate the old records for Compaq. Predicting next year s profit for existing customers. Without combining past HP/Compaq data, there would be no background on the combined company to use for effective prediction. Scoring: One for a reasonable example, one for explanation. 5 Data Mining Process / Cost The company has said we have the data in a data warehouse, all you need to do is install a data mining tool and run it. Shouldn t take you more than a couple of days. 5.1 True or False? (0.1 minute, 1 point) False 5.2 Reasoning (8 minutes, 4 points) How would you justify your answer to 5.1? Remember, you are out to convince the company - not me. Give a description of the resources you would need and the reasoning you would use estimate the time/cost required. Also suggest what resources or arguments you might use to convince the company you are correct. You don t need to do an estimate, just say briefly how you would go about it. 3

5 Decision trees capture such correlation. For example, we could have Naï ve as one node of a decision tree. The yes branch could go on to Bayes. The yes from that would have data mining as the class. A no branch from either node would not lead to data mining. Scoring: One point for naming a method that handles your objection to Naïve Bayes, one for a solid discussion of how it handles the problem. 5

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