Non-metric conceptual clustering: a new tool for investigating urban quality of life (1) - Patrick H. Buckley; Debnath Mookherjee (Cybergeo, 1999)
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.