Castilli, W.
http://repub.eur.nl/ppl/4852/
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RePub, Erasmus University RepositoryGlobal Optimization strategies for two-mode clustering
http://repub.eur.nl/pub/7022/
Mon, 07 Nov 2005 00:00:01 GMT<div>Rosmalen, J.M. van</div><div>Groenen, P.J.F.</div><div>Trejos, J.</div><div>Castilli, W.</div>
Two-mode clustering is a relatively new form of clustering that clusters both rows and
columns of a data matrix. To do so, a criterion similar to k-means is optimized. However,
it is still unclear which optimization method should be used to perform two-mode
clustering, as various methods may lead to non-global optima. This paper reviews and
compares several optimization methods for two-mode clustering. Several known
algorithms are discussed and a new, fuzzy algorithm is introduced. The meta-heuristics
Multistart, Simulated Annealing, and Tabu Search are used in combination with these
algorithms. The new, fuzzy algorithm is based on the fuzzy c-means algorithm of Bezdek
(1981) and the Fuzzy Steps approach to avoid local minima of Heiser and Groenen
(1997) and Groenen and Jajuga (2001). The performance of all methods is compared in a
large simulation study. It is found that using a Multistart meta-heuristic in combination
with a two-mode k-means algorithm or the fuzzy algorithm often gives the best results.
Finally, an empirical data set is used to give a practical example of two-mode clustering.