Finding Groups in Data: An Introduction to Cluster Analysis by Leonard Kaufman, Peter J. Rousseeuw

Finding Groups in Data: An Introduction to Cluster Analysis



Download Finding Groups in Data: An Introduction to Cluster Analysis




Finding Groups in Data: An Introduction to Cluster Analysis Leonard Kaufman, Peter J. Rousseeuw ebook
Format: pdf
Publisher: Wiley-Interscience
ISBN: 0471735787, 9780471735786
Page: 355


If the data were analyzed through cluster analysis, cat and dog are more likely to occur in the same group than cat and horse. Hoboken, New Jersey: Wiley; 2005. An Introduction to Cluster Analysis. Kaufman L, Rousseeuw PJ: Finding groups in data: an introduction to cluster analysis. The techniques of global partitioning of the data, such as K-means, partitioning around medoids, various flavors of hierarchical clustering, and self-organized maps [1-4], have provided the initial picture of similarity in the gene expression profiles, Another approach to finding functionally relevant groups of genes is network derivation, which has been popular in the analysis of gene-gene and protein-protein interactions [6-10], and is also applicable to gene expression analysis [11,12]. In order to solve the cluster analysis problem more efficiently, we presented a new approach based on Particle Swarm Optimization Sequence Quadratic Programming (PSOSQP). Kaufman L, Rousseeuw PJ: Finding Groups in Data. First, we created the optimization Second, PSOSQP was introduced to find the maximal point of the VRC. The experimental dataset contained 400 data of 4 groups with three different levels of overlapping degrees: non-overlapping, partial overlapping, and severely overlapping. New York: John Wiley & Sons; 1990. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability 1967, 1:281-297. You can This is a general introduction to free-listing. Researchers have noted that people find it a natural task. Fraley C, Raftery AE: Model-based clustering, discriminant analysis, and density estimation.