Affiliation:
1. School of Computer & Information, Anhui Polytechnic University, Wuhu, 241000, China
2. School of Optoelectronics, Beijing Institute of Technology, Beijing, 100081, China
Abstract
The first synchronization clustering (SynC) algorithm based on an extensive Kuromato model was presented in 2010. In 2017, an effective synchronization clustering (ESynC) algorithm, inspired by SynC algorithm and a linear version of Vicsek model, was proposed. When facing complex data distributions, ESynC algorithm may regard an irregular and whole cluster as some micro-clusters. To conquer this shortcoming, a combined clustering algorithm based on ESynC algorithm and a merging judgement process of micro-clusters (CESynC) is presented. CESynC algorithm first uses ESynC algorithm to detect clusters or micro-clusters, then merges those conjoint micro-clusters by using a merging judgement process. For some datasets that ESynC and SynC cannot detect correct clusters, CESynC can capture natural clusters. From the simulation experiments, we observe that CESynC can get better (or the same) clustering results than (or as) that of ESynC in many cases. We also observe that the clustering results of CESynC and ESynC are often better than that of SynC. Therefore, we can say CESynC can often obtain better clustering quality than ESynC and SynC in some kinds of datasets. Further comparison experiments with some classical clustering algorithms demonstrate the clustering effect of CESynC.
Funder
Anhui Polytechnic University
Natural Science Research in Colleges and Universities of Anhui Province
the University Synergy Innovation Program of Anhui Province
Publisher
World Scientific Pub Co Pte Lt
Subject
Artificial Intelligence,Information Systems,Control and Systems Engineering,Software
Cited by
3 articles.
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