Optimization Strategies for Two-Mode Partitioning
Journal of Classification , Volume 26 - Issue 2 p. 155- 181
Two-mode partitioning is a relatively new form of clustering that clusters both rows and columns of a data matrix. In this paper, we consider deterministic two-mode partitioning methods in which a criterion similar to k-means is optimized. A variety of optimization methods have been proposed for this type of problem. However, it is still unclear which method should be used, as various methods may lead to non-global optima. This paper reviews and compares several optimization methods for two-mode partitioning. Several known methods are discussed, and a new fuzzy steps method is introduced. The fuzzy steps method is based on the fuzzy c-means algorithm of Bezdek (1981) and the fuzzy steps approach of Heiser and Groenen (1997) and Groenen and Jajuga (2001). The performances of all methods are compared in a large simulation study. In our simulations, a two-mode k-means optimization method most often gives the best results. Finally, an empirical data set is used to give a practical example of two-mode partitioning.
|Journal of Classification|
|Organisation||Erasmus Research Institute of Management|
van Rosmalen, J.M, Groenen, P.J.F, Trejos, J, & Castillo, W. (2009). Optimization Strategies for Two-Mode Partitioning. Journal of Classification, 26(2), 155–181. doi:10.1007/s00357-009-9031-2