Mon premier blog

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

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

Kaufman L, Rousseeuw PJ: Finding Groups in Data. The aims of Module 1 are: To give a broad overview of how research questions might be answered through . Clustering is a powerful tool for automated analysis of data. This study uses a two-step cluster analysis of opinion variables to segment consumers into four market segments (Potential activists, Environmentals, Neutrals, and National interests). Our goal was to establish an organizational classification which would group PHC organizations based on their common characteristics. It addresses the following general problem: given a set of entities, find subsets, or clusters, which are homogeneous and/or well separated (cf. Simply stated, clustering involves Kaufman L, Rousseeuw PJ (2005) Finding groups in data: an introduction to Cluster Analysis. It is the art of finding groups in data and relies on the meaningful interpretation of the researcher or classifier [16]. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons, Hoboken, NJ, USA, 2005. New York: John Wiley & Sons; 1990. 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. An Introduction to Cluster Analysis. The organizational data were analyzed .. 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. Cluster profiles are examined . Fraley C, Raftery AE: Model-based clustering, discriminant analysis, and density estimation. The information obtained from the organizational survey enabled us to characterize PHC organizations.