Finding Groups in Data: An Introduction to Cluster Analysis by Leonard Kaufman, Peter J. Rousseeuw

Finding Groups in Data: An Introduction to Cluster Analysis



Finding Groups in Data: An Introduction to Cluster Analysis book




Finding Groups in Data: An Introduction to Cluster Analysis Leonard Kaufman, Peter J. Rousseeuw ebook
Page: 355
Format: pdf
ISBN: 0471735787, 9780471735786
Publisher: Wiley-Interscience


It is undoubtedly both an excellent inroduction to and a. Finding Groups in Data: An Introduction to Cluster Analysis (Wiley. Clustering is a powerful tool for automated analysis of data. The method uses a robust correlation measure to cluster related ports and to control for the .. Finding groups in data, an introduction to cluster analysis. The analysis documented in this report is a large-scale application of statistical outlier detection for determining unusual port- specific network behavior. The aims of Module 1 are: To give a broad overview of how research questions might be answered through . The Wiley–Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. In Section 3.2, we introduce the Minimum Covariance Distance (MCD) method for robust correlation. 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. 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. It addresses the following general problem: given a set of entities, find subsets, or clusters, which are homogeneous and/or well separated (cf. In Section 3.3, we introduce local hierarchical clustering for finding groups of related ports. Applied multivariate statistical analysis, (3rd ed.).