The two-step Clustering Component is a cluster analysis designed to handle large datasets which can be a combination of both continuous and categorical variables. Quantitative variables with different scale units and nominal scaled variables may be simultaneously analyzed. The user must decide to handle ordinal variables either as continuous or as categorical. Two step cluster analysis procedure uses a likelihood distance measure which assumes that variables in the cluster model are independent, in other words the variables should not be dependent to one another.                                                                                                 Two step cluster analysis is named so because it consists of two steps, the first step, where the total observations are clustered into small sub clusters and later on they are treated as individual observations. The distance criteria will determine whether the observation is joined to existing cluster or form a new cluster. The algorithm of two-step cluster is capable to determine the number of clusters automatically. The second step is grouping, where analysis is performed with the sub clusters created and they are grouped into the required number of clusters. SPSS uses the agglomerative hierarchical clustering method which works efficiently through the auto-cluster feature in the two-step clustering component. This simulation which is consistently accurate and scalable in performance and shows the automatic procedure of determining the number of clusters even when working with large data files.                                                                                                                        For example if we analyze the information about the customers of a bank, dividing them into three clusters, using SPSS two step cluster method. Two step creates three customers’ profiles. The largest group contains skilled customers, whose purpose of the loan is education or business. The second group consists in persons with real estate, but mostly unemployed, which asked for a credit for retraining or for household goods. The third profile groups people with unknown properties, who make a request for a car or a television and then for education. The purpose of the analysis is reinforcing the company’s profits by managing its clients more effectively.


By Joel Joby George (1521058)


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