Fast global k-means clustering based on local geometrical information
The fast global k-means (FGKM) clustering algorithm is one of the most effective approaches for resolving the local convergence of the k-means clustering algorithm. Numerical experiments show that it can effectively determine a global or near global minimizer of the cost function. However, the FGKM algorithm needs a large amount of computational time or storage space when handling large data sets. To overcome this deficiency, a more efficient FGKM algorithm, namely FGKM+A, is developed in this paper. In the development, we first apply local geometrical information to describe approximately the set of objects represented by a candidate cluster center. On the basis of the approximate description, we then propose an acceleration mechanism for the production of new cluster centers. As a result of the acceleration, the FGKM+A algorithm not only yields the same clustering results as that of the FGKM algorithm but also requires less computational time and fewer distance calculations than the FGKM algorithm and its existing modifications. The efficiency of the FGKM+A algorithm is further confirmed by experimental studies on several UCI data sets.
global k-means clustering local geometrical information cluster analysis optimization computational complexity