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An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling

1975; Elsevier BV; Volume: 12; Issue: 3 Linguagem: Inglês

10.1016/0022-2496(75)90028-0

1096-0880

Ronald L. Breiger, Scott A. Boorman, Phipps Arabie,

Data Management and Algorithms

A method of hierarchical clustering for relational data is presented, which begins by forming a new square matrix of product-moment correlations between the columns (or rows) of the original data (represented as an n × m matrix). Iterative application of this simple procedure will in general converge to a matrix that may be permuted into the blocked form [−111−1]. This convergence property may be used as the basis of an algorithm (CONCOR) for hierarchical clustering. The CONCOR procedure is applied to several illustrative sets of social network data and is found to give results that are highly compatible with analyses and interpretations of the same data using the blockmodel approach of White (White, Boorman & Breiger, 1976). The results using CONCOR are then compared with results obtained using alternative methods of clustering and scaling (MDSCAL, INDSCAL, HICLUS, ADCLUS) on the same data sets.