![]() To use the K-Means clustering option, click Classify > K-Means Cluster from the Analyze list on the main menu of the Data Editor. This process is repeated until the change in the center positioning stops or becomes so small as not to matter. Because the first point was randomly chosen, you can see that the new center is different.Īfter you find the new centroid, the distance from all points is calculated again and the members are regrouped based on the moved centroid. Regardless, the next substep is to find the center point (usually called the centroid) of each group. In most cases, that is the Euclidean distance in multidimensional space. Then, the algorithm groups members into the class of the point that is closest to the member. The key concept of the K-Means algorithm to understand is that it randomly picks a center point for each class. K-Means is a popular clustering algorithm. Spreadsheet data in the SPSS Statistics Data Editor K-Means The data shown in Figure 1 came from a spreadsheet and read into the SPSS Data Viewer. (Tree in this case really is more broadly called Decision Trees.)Īfter your data is in the spreadsheet and brought into the SPSS Statistics Data Editor, you can choose which algorithm to work with. I concentrate on two of the algorithms for this article: K-Means and Tree. You can also throw a neural network on that list, but in SPSS Statistics, that algorithm is listed separately.Įach of these algorithms has strengths and weaknesses, depending on the amount of data you have, the type or characteristics of the variables, and your end purpose in classifying the data. These are the top hits of the clustering algorithms in general use. It has more than this article can cover in the allotted space and more than you probably want to read about in one sitting, but here’s the quick list: SPSS Statistics has several statistical algorithms for creating segmentation. SPSS Statistics methods to create segmentation models You might have to add this type of data manually. Examples of this type of information include an assessment of the relationship quality from your salesperson, or a rating that is based on the number of returns or complaints. Third, there are characteristics of your customers that do not come from any centralized database. Sometimes, you create new calculations in queries to get new numbers. You might already have such behavioral characteristics of your customers available now. Often, you use queries to extract this information from your enterprise resource planning system. These behavior characteristics are data points, such as, the number of orders in a month, the average value of orders, and the number of days to pay. Second, there are characteristics of your customer’s behavior. Where is the customer located? What is the customer’s industry? How many employees does it have? What is its revenue? How many regions is the customer in? These characteristics are the demographic characteristics of your customers, and your customer relationship management (CRM) systems often already contain these data points. First, there are the characteristics that most people usually come up with first. I think of the types of customer characteristics as falling into one of three categories. The first question typically is, which characteristics do you use? You begin by gathering all of the relevant and required information about your customers into one spreadsheet. As you analyze results and talk to other people, you can add new fields, and then run the modeling process again. IBM SPSS Statistics makes it easy to use that spreadsheet, which is good, because you can do so repeatedly. You can use it to collect data from many sources easily, distribute it for review, and edit it to increase accuracy. Although not the most advanced or technically sophisticated method, you can easily gather the data elements about each customer in a spreadsheet.Ī spreadsheet is useful when you create customer segmentation models. Unless your company is a major retailer, you can probably list your customers in a single spreadsheet. Updated Novem| Published November 13, 2012
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