K-means clustering procedures are frequently used to identify homogeneous market segments on the basis of a set of descriptor variables. In practice, however, market research analysts often desire both homogeneous market segments and good explanation of an exogenous response variable. Unfortunately, the relationship between these two objective criteria can be antagonistic, and it is often difficult to find clustering solutions that yield adequate levels for both criteria. The authors present a simulated annealing heuristic for solving bicriterion partitioning problems related to these objectives. A large computational study and an empirical demonstration reveal the effectiveness of the methodology. The authors also discuss limitations and extensions of the method.

combinatorial data analysis, least-squares unidimensional scaling, branch-and-bound, dynamic programming,

combinatorial optimization, multiobjective programming, seriation,

No abstract

Combinatorial data analysis, matrix permutation, dynamic programming, heuristics,