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

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

United Kingdom The primary objective in both cases was to examine the class separability in order to get an estimate of classification complexity. My research question is about elderly people and I have to find out underlying groups. In Module 1 we look at quantitative research and how we collect data, in order to provide a firm foundation for the analyses covered in later modules. The unsupervised classification of these data into functional groups or families, clustering, has become one of the principal research objectives in structural and functional genomics. The aims of Module 1 are: To give a broad overview of how research questions might be answered through . It addresses the following general problem: given a set of entities, find subsets, or clusters, which are homogeneous and/or well separated (cf. Affect inference in learning environments: a functional view of facial affect analysis using naturalistic data. 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]. The data comes from a questionnaire. When individuals form groups or clusters, we might expect that two randomly selected individuals from the same group will tend to be more alike than two individuals selected from different groups. Clustering is a powerful tool for automated analysis of data.