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

The data comes from a questionnaire. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons, Hoboken, NJ, USA, 2005. Our goal was to establish an organizational classification which would group PHC organizations based on their common characteristics. The image below is a sample of how it groups: You may ask yourself. My research question is about elderly people and I have to find out underlying groups. If you want to find part 1 and 2, you can find them here: Data Mining Introduction In this tutorial we are going to create a cluster algorithm that creates different groups of people according to their characteristics. The organizational data were analyzed .. When should I use decision tree and when to use cluster algorithm? 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 information obtained from the organizational survey enabled us to characterize PHC organizations. There is a nice accuracy graph that the SQL Server Analysis Services (SSAS) uses to measure that.