Heng Cao, Haifeng Xi, et al.
WSC 2003
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.
Heng Cao, Haifeng Xi, et al.
WSC 2003
R.B. Morris, Y. Tsuji, et al.
International Journal for Numerical Methods in Engineering
Jonathan Ashley, Brian Marcus, et al.
Ergodic Theory and Dynamical Systems
Jaione Tirapu Azpiroz, Alan E. Rosenbluth, et al.
SPIE Photomask Technology + EUV Lithography 2009