An effective multi-level synchronization clustering method based on a linear weighted Vicsek model

Abstract

To conquer the shortcoming that general clustering methods cannot process big data in the main memory, this paper presents an effective multi-level synchronization clustering (MLSynC) method by using a framework of “divide and collect” and a linear weighted Vicsek model. We also introduce two concrete implementations of MLSynC method, a two-level framework algorithm and a recursive algorithm. MLSynC method has a different process with SynC algorithm, ESynC algorithm and SSynC algorithm. By the theoretic analysis, we find the time complexity of MLSynC method is less than SSynC. Simulation and experimental study on multi-kinds of data sets validate that MLSynC method not only gets better local synchronization effect but also needs less iterative times and time cost than SynC algorithm. Moreover, we observe that MLSynC method not only needs less time cost than ESynC and SSynC, but also almost gets the same local synchronization effect as ESynC and SSynC if the partition of the data set is proper. Further comparison experiments with some classical clustering algorithms demonstrate the clustering effect of MLSynC method.