An effective synchronization clustering algorithm

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• 作者：Xinquan Chen
• 关键词：Clustering ; Synchronization ; Kuramoto model ; A linear version of Vicsek model ; Near neighbor point set
• 刊名：Applied Intelligence
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：46
• 期：1
• 页码：135-157
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence (incl. Robotics); Mechanical Engineering; Manufacturing, Machines, Tools;
• 出版者：Springer US
• ISSN：1573-7497
• 卷排序：46

文摘

This paper presents an Effective Synchronization Clustering (ESynC) algorithm using a linear version of Vicsek model. The development of ESynC algorithm is inspired by Synchronization Clustering (SynC) algorithm and Vicsek model. After some analysis and experimental comparison, we observe that ESynC algorithm based on the linear version of Vicsek model can get better local synchronization effect than SynC algorithm based on an extensive Kuramoto model and a similar synchronization clustering algorithm based on the original version of Vicsek model. By some simulated experiments of some artificial data sets, eight UCI data sets, and three picture data sets, we observe that ESynC algorithm not only gets better local synchronization effect but also needs less iterative times and time cost than SynC algorithm. We also introduce an Improved ESynC algorithm (IESynC algorithm) in time cost by combining multidimensional grid partitioning method and Red-Black tree structure. By some simulated experiments, we observe that IESynC algorithm can get some improvement of time cost than ESynC algorithm in some data sets. Extensive comparison experiments with some class clustering algorithms demonstrate that our two algorithms can often get acceptable clustering results in many cases. At last, it gives several solid and insightful future research suggestions.