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

If the data were analyzed through cluster analysis, cat and dog are more likely to occur in the same group than cat and horse. To extract more topological information— in particular, to get the homology groups— we need to do some more work. In addition to the edges of the graph, we will . You can also use cluster analysis to summarize data rather than to find "natural" or "real" clusters; this use of clustering is sometimes called dissection. Download An Introduction to Genetic Analysis Griffiths Hardcover Book. The purpose of cluster analysis is to place objects into groups, or clusters, suggested by the data, not defined by a priori, such that objects in a given cluster tend to be similar to each other in some sense, and objects in different clusters tend to be dissimilar. Researchers have noted that people find it a natural task. Cluster analysis is special case of TDA. An Introduction to Genetic Analysis & CD-Rom [Anthony J.F. You can This is a general introduction to free-listing. I think Ron Atkin introduced this stuff in the early 1970′s with his q-analysis (see http://en.wikipedia.org/wiki/Q-analysis). The basic idea of TDA is to describe the “shape of the data” by finding clusters, holes, tunnels, etc. Leonard Kaufman and Peter Rousseeuw (2005), Finding Groups in Data: An Introduction to Cluster Analysis, Wiley Series in Probability and Statistics, 337 p.