Martin Charles Golumbic, Renu C. Laskar
Discrete Applied Mathematics
In this paper, a new Global k-modes (GKM) algorithm is proposed for clustering categorical data. The new method randomly selects a sufficiently large number of initial modes to account for the global distribution of the data set, and then progressively eliminates the redundant modes using an iterative optimization process with an elimination criterion function. Systematic experiments were carried out with data from the UCI Machine learning repository. The results and a comparative evaluation show a high performance and consistency of the proposed method, which achieves significant improvement compared to other well-known k-modes-type algorithms in terms of clustering accuracy.
Martin Charles Golumbic, Renu C. Laskar
Discrete Applied Mathematics
I.K. Pour, D.J. Krajnovich, et al.
SPIE Optical Materials for High Average Power Lasers 1992
Corneliu Constantinescu
SPIE Optical Engineering + Applications 2009
John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence