Finding Groups in Data: An Introduction to

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

The aims of Module 1 are: To give a broad overview of how research questions might be answered through . 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. Cluster analysis is one of those techniques I don't get to use very often. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. Unlike the evaluation of supervised classifiers, which can be conducted using well-accepted objective measures and procedures, Relative measures try to find the best clustering structure generated by a clustering algorithm using different parameter values. About once every couple of years someone will be doing a study of types of companies, patients or clients and have a need for a cluster analysis. Cluster analysis or clustering is the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other than to those in other clusters. Because the clustering method failed to separate the patient data into groups by obvious traditional physiological definitions these results confirm our hypothesis that clustering would find meaningful patterns of data that were otherwise impossible to physiologically discern or classify using traditional clinical definitions. 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. Clustering is a main task of explorative data mining, and a common technique for statistical data analysis used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. Cluster analysis, the most widely adopted unsupervised learning process, organizes data objects into groups that have high intra-group similarities and inter-group dissimilarities without a priori information.