| The Models of Similarity and Association Contributor(s): Corter, James E. (Author), Lewis-Beck, Michael S. (Introduction by) |
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ISBN: 0803957076 ISBN-13: 9780803957077 Publisher: Sage Publications OUR PRICE: $39.90 Product Type: Paperback Published: April 1996 Annotation: Clustering and tree models are widely used in the social and biological sciences to analyze similarity relations. Tree Models of Similarity and Association describes how matrices of similarities or associations among entities can be modeled using trees, and to explain some of the issues that arise in performing such analyses and correctly interpreting the results. James E. Corter clearly distinguishes ultrametric trees (fit by the techniques widely known as "hierarchical clustering") from additive trees and discusses how specific aspects of each type of tree can be interpreted through the use of applications as examples. He concludes with a discussion of when tree models might be preferable to spatial geometric models, such as those fit by multidimensional scaling (MDS) or principal components analysis (PCA). |
| Additional Information |
| BISAC Categories: - Social Science | Methodology - Medical - Social Science | Research |
| Dewey: 300.151 |
| LCCN: 95050165 |
| Series: Quantitative Applications in the Social Sciences |
| Physical Information: 0.17" H x 6.04" W x 7.96" (0.19 lbs) 72 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Clustering and tree models are being widely used in the social and biological sciences to analyze similarity relations. This volume describes how matrices of similarities or associations among entities can be modelled using trees, and explains some of the issues that arise in performing such analyses and interpreting the results correctly. James E Corter distinguishes ultrametric trees from additive trees and discusses how specific aspects of each type of tree can be interpreted through the use of applications as examples. He concludes with a discussion of when tree models might be preferable to spatial geometric models. |
Contributor Bio(s): Corter, James E.: - Scholarly Interests Computational models of human learning and categorization. Judgment and decision-making. Clustering and scaling methods for multivariate data. Statistics expertise and probability problem-solving. Evaluation of educational technology innovations. |