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

Jolliffe IT: Principal Component Analysis. Introduction 1.1 What is cluster analysis? New York: John Wiley & Sons; 1990. Hoboken, NJ: John Wiley & Sons, Inc; 1990:1986. Cluster analysis is a collection of statistical methods, which identifies groups of samples that behave similarly or show similar characteristics. Kaufman L, Rousseeuw PJ: Finding Groups in Data. It is a Clustering customer behavior data for segmentation; Clustering transaction data for fraud analysis in financial services; Clustering call data to identify unusual patterns; Clustering call-centre data to identify outlier performers (high and low) Please do let us know if you find them useful. Clustering is the process of breaking down a large population that has a high degree of variation and noise into smaller groups with lower variation. The data comes from a questionnaire. 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]. Hierarchical Cluster Analysis Some Basics and Algorithms 1. My research question is about elderly people and I have to find out underlying groups. An Introduction to Cluster Analysis. 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. Fraley C, Raftery AE: Model-based clustering, discriminant analysis, and density estimation. Cluster analysis is called Q-analysis (finding distinct ethnic groups using data about believes and feelings1), numerical taxonomy (biology), classification analysis (sociology, business, psychology), typology2 and so on. Kaufman L, Rousseeuw PJ: Finding Groups in Data: An Introduction to Cluster Analysis. 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.