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Finding Groups in Data: An Introduction to Cluster Analysis Leonard Kaufman, Peter J. Rousseeuw
Publisher: Wiley-Interscience

In Module 1 we look at quantitative research and how we collect data, in order to provide a firm foundation for the analyses covered in later modules. In Section 3.2, we introduce the Minimum Covariance Distance (MCD) method for robust correlation. Kaufman L, Rousseeuw PJ: Finding Groups in Data. Introduction of Data mining: Data mining is a training devices that automatically search large stores of data to find patterns and trends that go beyond simple analysis. Fraley C, Raftery AE: Model-based clustering, discriminant analysis, and density estimation. The method uses a robust correlation measure to cluster related ports and to control for the .. 4 Centralisation of wage bargaining. Data mining uses sophisticated mathematical algorithms that segment the Clustering: Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups. The aims of Module 1 are: To give a broad overview of how research questions might be answered through . The analysis documented in this report is a large-scale application of statistical outlier detection for determining unusual port- specific network behavior. When individuals form groups or clusters, we might expect that two randomly selected individuals from the same group will tend to be more alike than two individuals selected from different groups. 3 Collectivisation of wage bargaining. 5.1 Direct government involvement in wage setting. 18 Our data provide information from 1995 and 2006 for 23 European countries, plus the US and Japan. New York: John Wiley & Sons; 1990. An Introduction to Cluster Analysis. 5 Wage bargaining coordination and government involvement. In Section 3.3, we introduce local hierarchical clustering for finding groups of related ports.