Jain A.K., Dubes R.C. Algorithms for Clustering Data

Prentice Hall, 1988, -334 p.Cluster analysis is an importanllechnique in the rapidly growing field known as exploratory data analysis and is being applied in a variety of engineering and scientific disciplines such as biology, psychology. medicine, marketing, computer vision. and remote sensing. Cluster analysis organizes data by abstracting underlying structure either as a grouping of individuals or as a hierarchy of groups. The representation can then be investigated to see if the data group according to preconceived ideas or to suggest new experiments. Cluster analysis is a tool for exploring the structure of the data that does nol require the assumptions common to most statistical methods. II is called "unsupervised learning" in the literature of pauem recognition and artificial intelligence.
This book will be useful for those in the scientific community who gather data and seek tools for analyzing and interpreting data. II will be a valuable reference for scientists in a variety of disciplines and can serve as a textbook for a graduate course in exploratory data analysis as well as a supplemental text in courses on researeh methodology. pattern recognition, image processing. and remote sensing. The book emphasizes infonnal algorithms for clustering data. and interpreting results. Graphical procedures and other tools for visually representing data are introduced both to evaluate the results of clustering and to explore data. Mathematical and statistical theory are introduced only when necessary.
Most existing books on cluster analysis are written by mathematicians. numer ical taxonomislS. social scientists. and psychologists who emphasize either the methods that lend themselves to mathematical treatment or the applications in their particular area. Our book strives for a sense of completeness and for a balanced presentation. We bring together many results that are scattered through the literature of several fields. lbe most unique feature of this book is its thorough, understandable treatment of cluster validity, or the objective validation of the results of cluster analysis, from the application viewpoint.Data Representation
Clustering Methods and Algorithms
Cluster Validity
Applications
A Pattern Recognition
B Distributions
C Linear Algebra
D Scatter Matrices
E Factor Analysis
F Multivariate Analysis of Variance
G Graph Theory
H Algorithm for Generating Clustered Data