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• W205616124 abstract "A method is proposed for the simultaneous hierarchical clustering of row, column, and block elements of a three-way three-mode data matrix. The procedure generalizes the two-mode error-variance approach (Eckes & Orlik, 1993) to the three-mode case. At each step of the agglomerative process, the algorithm merges those clusters whose fusion results in the smallest possible increase in an internal heterogeneity measure. Optionally, the procedure yields an overlapping cluster solution by assigning further row and/or column and/or block elements to a given number of clusters. An application to a data set drawn from object perception research illustrates the approach. Finally, several indications of three-mode clustering are discussed." @default.
• W205616124 created "2016-06-24" @default.
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• W205616124 date "1994-01-01" @default.
• W205616124 modified "2023-09-23" @default.
• W205616124 title "Three-Mode Hierarchical Cluster Analysis of Three-Way Three-Mode Data" @default.
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• W205616124 doi "https://doi.org/10.1007/978-3-642-46808-7_19" @default.
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