Metrics on spaces of finite trees
| Autor: | Donald C. Olivier, Scott A. Boorman |
|---|---|
| Rok vydání: | 1973 |
| Předmět: |
Structure (mathematical logic)
business.industry Applied Mathematics Equivalence of metrics Machine learning computer.software_genre Hierarchical clustering Set (abstract data type) Tree (data structure) Tree structure Artificial intelligence Componential analysis Derived set business computer General Psychology Mathematics |
| Zdroj: | Journal of Mathematical Psychology. 10:26-59 |
| ISSN: | 0022-2496 |
| DOI: | 10.1016/0022-2496(73)90003-5 |
| Popis: | With the increasing popularity of hierarchical clustering methods in behavioral science, there is a need for ways of quantitatively comparing different tree structures on the same set of items. We employ lattice-theoretic methods to construct a variety of metrics on spaces of trees and to analyze their properties. Certain of these metrics are applied to data from Fillenbaum and Rapoport (1971) on the semantic structure of common English kin terms. This application shows that tree metrics can be used to select a componential analysis which is maximally consistent with an empirically derived set of trees. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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