Fast Adaptive Clustering by Synchronization on Large Scale Datasets

The existing synchronization clustering algorithm Sync regards each attribute of a sample as a phase oscillator in the synchronization process. As a result, the algorithm has higher time complexity and can not be well used on large scale datasets. To solve this problem, we propose a novel fast adaptive clustering algorithm FAKCS in this paper. Firstly, FAKCS introduces a method based on RSDE and CCMEB technology to extract the samples from the original dataset. Then it begins clustering adaptively by using the DaviesBouldin cluster criterion and the new order parameter which can observe the degree of local synchronization. Moreover, the relationship between the new order parameter and KDE is found in this paper, which reveals the probability density nature of local synchronization. FAKCS can detect clusters of arbitrary shape, number and density on large scale datasets without setting cluster number previously. The effectiveness of the proposed method has been demonstrated in image segmentation examples and experiments on large UCI datasets.