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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
Publisher: Wiley-Interscience
Page: 355
ISBN: 0471735787, 9780471735786
Format: pdf

One of the ultimate goals of .. The organizational data were analyzed .. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. Most of our sensory neocortex is engaged in the processing of visual inputs that we gather from our surroundings. The grouping process implements a clustering methodology called "Partitioning Around Mediods" as detailed in chapter 2 of L. Kaufman L, Rousseeuw PJ: Finding groups in data: an introduction to cluster analysis. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons, Hoboken, NJ, USA, 2005. Humans are essentially a visual species. In contrast to supervised machine learning, unsupervised learning such as cluster analysis can be used independently of prior knowledge to find groups within data. 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. Hoboken, New Jersey: Wiley; 2005. Our goal was to establish an organizational classification which would group PHC organizations based on their common characteristics. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability 1967, 1:281-297. The information obtained from the organizational survey enabled us to characterize PHC organizations. Not surprisingly, visualization techniques are at the heart of science and engineering [1].