A novel k-Means based on spatial density similarity measurement
Wei Xue, Rong-li Yang, Xiao-yu Hong, Nan Zhao, Shou-gang Ren · 2017
k-Means clustering algorithm is widely used in many machine learning tasks. However, the classic k-Means clustering algorithm has poor performance on classification of non-convex data sets. We find that k-Means effect depends heavily on the measurement of similarity between instances of the datasets. In novel algorithm, we define the new distance measurement of scalable spatial density similarity in data sets, and propose a cluster-center iterative model in the algorithm. Experimental results show that compared with Euclidean distance based k-Means, our proposed algorithm with spatial density similarity measurement generally perform more accurate on several synthetic and real-world datasets.