An Introduction to Numerical Classification describes the rationale of numerical analyses by means of geometrical models or worked examples without possible extensive algebraic symbolism. Organized into 13 chapters, the book covers both the taxonomic and ecological aspects of numerical classification. After briefly presenting different terminologies used in this work, the book examines several types of biological classification, including classification by structure, proximity, similarity, and difference. It then describes various ecological and taxonomic data manipulations, such as data reduction, transformation, and standardization. Other chapters deal with the criteria for best computer classification and the complexities and difficulties in this classification. These difficulties are illustrated by reference to studies of the ""bottom communities"" of benthic marine invertebrates, ranging across the entire field from the sampling program and nature of the data to problems over the type of computer used. The concluding chapters consider some of the measures of diversity and the interpretations which have been made from them, as well as the relationship of diversity to classification. The concept and application in biological classification of various multivariate analyses are also discussed in these texts. Supplemental texts on the information measures, partitioning, and interdependence of data diversity are also provided. This book is of value to biologists and researchers who are interested in basic biological numerical classification.
Preface 1 Introduction Text 2 Classification by Structure A. Naming B. The Higher Categories: Arbitrary Divisions C. The Evolutionary Framework: "The New Systematics" D. Models Showing Taxonomic Relationships E. The Taxonomic Continuum F. Critique of Classic Taxonomy 3 Classification by Proximity A. Biogeographical Classification B. Ecological Classification 4 General Comments on Classification A. Continua in Nonbiological Situations B. What Classification Involves 5 Numerical Approaches to Classification A. Introduction B. Types of Data 6 Measures of Similarity and Difference A. General B. Coefficients of Similarity C. Coefficients of Association D. Euclidean Distance as a Dissimilarity Measure E. Information Theory Measures of Similarity/Dissimilarity F. Probabilistic Measures G. Further Properties of Similarity Measures 7 Reduction, Transformation, and Standardization of Data A. General B. Data Reduction C. Data Transformation D. Data Standardization E. Reduction, Transformation, and Standardization of Taxonomic Data F. Discussion of Data Manipulation 8 Similarity Matrices and their Analysis A. Visual Matrices—Trellis Diagrams B. Classificatory Strategies in General C. Monothetic Divisive Hierarchical Clustering Methods D. Agglomerative Polythetic Hierarchical Clustering Methods E. Nonhierarchical Clustering, Clumping, Graphs, and Minimum Spanning Trees 9 The Handling and Interpretation of the Results of Computer Classifications A. General Comparison and Interpretation of Results B. Application of Significance Tests C. Combination of Strategies D. Dendrograms 10 Difficulties in Numerical Classification A. Objectives in Classification B. Choice of Data C. Choice of Strategy D. Presentation of Results E. The Time Factor
- © Academic Press 1975
- 28th June 1975
- Academic Press
- eBook ISBN: