Abstract
An overview of cluster analysis techniques from a data mining point of view is given. This is done by a strict separation of the questions of various similarity and distance measures and related optimization criteria for clusterings from the methods to create and modify clusterings themselves. In addition to this general setting and overview, the second focus is used on discussions of the essential ingredients of the demographic cluster algorithm of IBM's Intelligent Miner, based Condorcet's criterion.
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Grabmeier, J., Rudolph, A. Techniques of Cluster Algorithms in Data Mining. Data Mining and Knowledge Discovery 6, 303–360 (2002). https://doi.org/10.1023/A:1016308404627
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DOI: https://doi.org/10.1023/A:1016308404627