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

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. I think Ron Atkin introduced this stuff in the early 1970′s with his q-analysis (see http://en.wikipedia.org/wiki/Q-analysis). We performed multivariate (exhaled NO as dependent variable) and k-means cluster analyses in a population of 169 asthmatic children (age ± SD: 10.5 ± 2.6 years) recruited in a monocenter cohort that was characterized in a cross-sectional .. 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. ACM San Francisco Bay Area Professional Chapter course. In addition to the edges of the graph, we will . Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability 1967, 1:281-297. The basic idea of TDA is to describe the “shape of the data” by finding clusters, holes, tunnels, etc. This course outline includes R introduction (including getting unstuck), Data Management, Graphics, and Statistical Analysis and Data Mining. To extract more topological information— in particular, to get the homology groups— we need to do some more work. Cluster analysis is special case of TDA. Hoboken, New Jersey: Wiley; 2005. Kaufman L, Rousseeuw PJ: Finding groups in data: an introduction to cluster analysis.