Finding the optimal solution to the problem of selecting clustering centers and improving the performance of existing density-based clustering algorithms, a novel clustering method is proposed in this paper. Our algorithm discovers data clusters according to cluster centers that are identified by a higher density than their nearby points and by a comparatively large distance from points with higher density, and then it finds optimal cluster centers by iteration based on genetic algorithm. We present an exponential density analysis to reduce the impact of model parameters and introduce a penalty factor in order to overcome the excursion of search region for accelerating convergence. Experiments on both artificial and UCI data sets reveal that our algorithm achieves results on Rand Statistic competitive with a variety of classical algorithms.
In order to improve the efficiency of rendering terrain based on digital elevation model (DEM), a mesh simplification algorithm based on vertex importance and hierarchical clustering tree is presented. The vertexes of terrain blocks are firstly trained using K-means clustering analysis, and then we select representative vertexes of each cluster according to vertex importance. Secondly, coarse meshes are constructed on the basis of these representative vertexes. Thirdly, we seam all coarse meshes. Finally, repeat the above steps until we accomplish the whole simplification process. For the new insertion point, a hierarchical clustering tree is used to record intermediate results, which is applied to view dependent rendering for terrain. Experiment show that, the algorithm improves the efficiency and reduces memory consumption. At the same time, it maintains geometric characteristics of terrain.