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

[1] Kaufman L and Rousseeuw PJ. Finally, we discuss the consequences of our findings for the experimental design of microbiota studies in murine disease models. A linear mixed-effects model, which accounts for the repeated measurements per cell (i.e., the annuli per cell), was fit to the data, to compare the number of dendrite intersections per annulus between cells within each cluster in retinas .. Finding Groups in Data: An Introduction to Cluster Analysis. Let's describe a generative model for finding clusters in any set of data. Cluster analysis or clustering is the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other than to those in other clusters. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. Clustering is a main task of explorative data mining, and a common technique for statistical data analysis used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. €�On Lipschitz embedding of finite metric spaces in Hilbert space”. We assume an infinite set of latent groups, where each group is described by some set of parameters. 3Cellular and Molecular Physiology, Penn State Retina Research Group, Penn State College of Medicine, Milton S. Hershey Medical Center, Hershey, Pennsylvania. 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. Kaufman L, Rousseeuw PJ: Finding Groups in Data: An Introduction to Cluster Analysis. Finding Groups in Data: an Introduction to Cluster Analysis.