A Novel Clustering Method Based on Quasi-Consensus Motions of Dynamical Multiagent Systems

This paper presents a novel approach for clustering, which is based on quasi-consensus of dynamical linear high-order multiagent systems. The graph topology is associated with a selected multiagent system, with each agent corresponding to one vertex. In order to reveal the cluster structure, the agents belonging to a similar cluster are expected to aggregate together. To establish the theoretical foundation, a necessary and sufficient condition is given to check the achievement of group consensus. Two numerical instances are furnished to illustrate the results of our approach.

[1]  Yisheng Zhong,et al.  Brief paper swarm stability of high-order linear time-invariant swarm systems , 2011 .

[2]  Shuang Gao,et al.  Wind power day-ahead prediction with cluster analysis of NWP , 2016 .

[3]  Wenwu Yu,et al.  Group consensus for heterogeneous multi-agent systems with parametric uncertainties , 2014, Neurocomputing.

[4]  Li Yu,et al.  Group consensus in multi-agent systems with hybrid protocol , 2013, J. Frankl. Inst..

[5]  C. Cooper,et al.  Cluster analysis of bone microarchitecture from high resolution peripheral quantitative computed tomography demonstrates two separate phenotypes associated with high fracture risk in men and women. , 2016, Bone.

[6]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[7]  F. Xiao,et al.  Consensus problems for high-dimensional multi-agent systems , 2007 .

[8]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Dongmei Xie,et al.  Necessary and sufficient condition for the group consensus of multi-agent systems , 2014, Appl. Math. Comput..

[10]  Angelo Cangelosi,et al.  Simulating the Evolution of Language , 2002, Springer London.

[11]  Ming He,et al.  Admissible output consensualization control for singular multi-agent systems with time delays , 2016, J. Frankl. Inst..

[12]  Daizhan Cheng,et al.  Consensus of multi-agent linear dynamic systems† , 2008 .

[13]  Chris H. Q. Ding,et al.  A min-max cut algorithm for graph partitioning and data clustering , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[14]  Randal W. Beard,et al.  Consensus seeking in multiagent systems under dynamically changing interaction topologies , 2005, IEEE Transactions on Automatic Control.

[15]  Ling Guo,et al.  Adaptive pinning control of cluster synchronization in complex networks with Lurie-type nonlinear dynamics , 2016, Neurocomputing.

[16]  Dong Liu,et al.  Estimating the optimal number of communities by cluster analysis , 2016 .

[17]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[18]  Xiao Fan Wang,et al.  Decentralized Adaptive Pinning Control for Cluster Synchronization of Complex Dynamical Networks , 2013, IEEE Transactions on Cybernetics.

[19]  D. Corneil,et al.  An Efficient Algorithm for Graph Isomorphism , 1970, JACM.

[20]  Lin Huang,et al.  Consensus of Multiagent Systems and Synchronization of Complex Networks: A Unified Viewpoint , 2016, IEEE Transactions on Circuits and Systems I: Regular Papers.

[21]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Cai Ning,et al.  Clustering by group consensus of unstable dynamic linear high-order multi-agent systems , 2015, 2015 34th Chinese Control Conference (CCC).

[23]  Agus Zainal Arifin,et al.  Image segmentation by histogram thresholding using hierarchical cluster analysis , 2006, Pattern Recognit. Lett..

[24]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[25]  Long Wang,et al.  Group consensus in multi-agent systems with switching topologies and communication delays , 2010, Syst. Control. Lett..

[26]  Alex Arenas,et al.  Synchronization reveals topological scales in complex networks. , 2006, Physical review letters.

[27]  Young Hoon Joo,et al.  Segmentalized FCM-based tracking algorithm for zigzag maneuvering target , 2015 .

[28]  Housheng Su,et al.  Adaptive cluster synchronisation of coupled harmonic oscillators with multiple leaders , 2013 .