Clustering Methodologies in Exploratory Data Analysis

Publisher Summary This chapter reviews cluster analysis and related topics or the formal study of classification schemata, whereby objects are grouped, or clustered, according to measured or perceived intrinsic characteristics. The objective of a cluster analysis is to uncover natural groupings, or types, to prod one's creativity and ingenuity, and initiate hypotheses about the phenomenon being studied. Cluster analysis has a heuristic nature that encourages the exploration of data. Taxonomists, social scientists, psychologists, biologists, statisticians, mathematicians, engineers, computer scientists, medical researchers, and others who handle real data have all contributed to clustering methodology. The chapter presents cross-disciplinary communication so that one application area can profit from the experiences of others. The literature of cluster analysis straddles all quantitative, scientific disciplines, as demonstrated by the remarkable variety. Emphasis is on new developments, especially in the verification and validation of clustering results. The intent is to provide an applications-oriented treatment of cluster analysis in the spirit of exploratory data analysis. The chapter focuses on the four operations highlighted by reviewing techniques for assessing the tendency of the data to cluster, performing the clustering itself, and evaluating the validity of the results. Data representation includes recognition of data type and scale, measures of proximity and affinity, normalization, various two-dimensional projections of data, methods of visualizing multidimensional data, techniques for creating scales to describe data, and related matters. The chapter introduces the concept of intrinsic dimensionality that helps determine an appropriate number of factors for representing data. The chapter recommends that a serious data analyst be conversant with as broad a range of data analysis techniques and programs as possible and be aware of the assumptions on which the techniques are based.

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