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

Humans are essentially a visual species. Clustering Large and High Dimensional data. Publications on Spatial Database and Spatial Data Mining at UMN . One of the ultimate goals of .. Finding Groups in Data: An Introduction to Cluster Analysis. Instructors can also use it as a textbook for an introductory course in cluster analysis or as source material for a graduate-level introduction to data mining. €� John Wiley & Sons, 1990 Collective Intelligence. Finding Groups in Data: An Introduction to Cluster Analysis Leonard Kaufman, Peter J. 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. In contrast to supervised machine learning, unsupervised learning such as cluster analysis can be used independently of prior knowledge to find groups within data. Not surprisingly, visualization techniques are at the heart of science and engineering [1]. Most of our sensory neocortex is engaged in the processing of visual inputs that we gather from our surroundings. 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. Finding Groups in Data: An Introduction to Cluster Analysis (Wiley. Knowledge Discovery and Data Mining (PAKDD. Kogan J., Nicholas C., Teboulle M. From this perspective, the above findings would suggest that DD is a single gene disease. Kaufman L, Rousseeuw P: Finding Groups in Data: An Introduction to Cluster Analysis (Wiley Series in Probability and Statistics). Audience The following groups will find this book a valuable tool and reference: applied statisticians; engineers and scientists using data analysis; researchers in pattern recognition, artificial intelligence, machine learning, and data mining; and applied mathematicians.