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

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). Simply stated, clustering involves Kaufman L, Rousseeuw PJ (2005) Finding groups in data: an introduction to Cluster Analysis. 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. Introduction 1.1 What is cluster analysis? Cluster analysis is called Q-analysis (finding distinct ethnic groups using data about believes and feelings1), numerical taxonomy (biology), classification analysis (sociology, business, psychology), typology2 and so on. It is the art of finding groups in data and relies on the meaningful interpretation of the researcher or classifier [16]. The information obtained from the organizational survey enabled us to characterize PHC organizations. Because the clustering method failed to separate the patient data into groups by obvious traditional physiological definitions these results confirm our hypothesis that clustering would find meaningful patterns of data that were otherwise impossible to physiologically discern or classify using traditional clinical definitions. Our goal was to establish an organizational classification which would group PHC organizations based on their common characteristics. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. Hierarchical Cluster Analysis Some Basics and Algorithms 1. Cluster analysis is a collection of statistical methods, which identifies groups of samples that behave similarly or show similar characteristics. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons, Hoboken, NJ, USA, 2005. Cluster profiles are examined . The organizational data were analyzed .. 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.