Non-metric conceptual clustering: a new tool for investigating urban quality of life (1)
Based on the use of a non-metric conceptual clustering technique, this empirical study explores the quality of life of a small metropolitan city. The RIFFLE program, developed at Western Washington University, is utilized to explicitly address the clustering algorithm where a subset n of m variables...
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| Formato: | article |
| Lenguaje: | DE EN FR IT PT |
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Unité Mixte de Recherche 8504 Géographie-cités
1999
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| Acceso en línea: | https://doaj.org/article/ac0b6789747d43e5911bf6914d6660c2 |
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| Sumario: | Based on the use of a non-metric conceptual clustering technique, this empirical study explores the quality of life of a small metropolitan city. The RIFFLE program, developed at Western Washington University, is utilized to explicitly address the clustering algorithm where a subset n of m variables creates an n dimension vector space partitioned into two or more clusters in each dimension. Applying a variation of Guttman's Lambda n variables and c clusters are reported by RIFFLE that predict the pattern. Non-metric conceptual clustering overcomes a number of problems common in traditional techniques such as data assumptions, relevancy and missing data. |
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