For one common kind of segmentation several algorithms apply and we don’t know which of them works best. Across data sets constructed to reflect a number of varying data conditions (number of segments, relative segment sizes, degree of segment separation, number of dimensions and number of variables per dimension) we test which of four robust segmentation algorithms fares best in terms of identifying the correct number of segments and in correctly assigning respondents to segments.
