A New Clustering Algorithm Based Upon Flocking On Complex Network

We have proposed a model based upon flocking on a complex network, and then developed two clustering algorithms on the basis of it. In the algorithms, firstly a \textit{k}-nearest neighbor (knn) graph as a weighted and directed graph is produced among all data points in a dataset each of which is regarded as an agent who can move in space, and then a time-varying complex network is created by adding long-range links for each data point. Furthermore, each data point is not only acted by its \textit{k} nearest neighbors but also \textit{r} long-range neighbors through fields established in space by them together, so it will take a step along the direction of the vector sum of all fields. It is more important that these long-range links provides some hidden information for each data point when it moves and at the same time accelerate its speed converging to a center. As they move in space according to the proposed model, data points that belong to the same class are located at a same position gradually, whereas those that belong to different classes are away from one another. Consequently, the experimental results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the rates of convergence of clustering algorithms are fast enough. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.

[1]  Reza Olfati-Saber,et al.  Flocking for multi-agent dynamic systems: algorithms and theory , 2006, IEEE Transactions on Automatic Control.

[2]  S. Strogatz Exploring complex networks , 2001, Nature.

[3]  Martin H. Levinson Linked: The New Science of Networks , 2004 .

[4]  Giandomenico Spezzano,et al.  An Adaptive Flocking Algorithm for Spatial Clustering , 2002, PPSN.

[5]  Levent Ertoz,et al.  A New Shared Nearest Neighbor Clustering Algorithm and its Applications , 2002 .

[6]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.

[7]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1998 .

[8]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[9]  Günes Erkan,et al.  Language Model-Based Document Clustering Using Random Walks , 2006, NAACL.

[10]  Steven V. Viscido,et al.  Self-Organized Fish Schools: An Examination of Emergent Properties , 2002, The Biological Bulletin.

[11]  Christopher J. Merz,et al.  UCI Repository of Machine Learning Databases , 1996 .

[12]  Giandomenico Spezzano,et al.  Swarm-Based Distributed Clustering in Peer-to-Peer Systems , 2005, Artificial Evolution.

[13]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

[14]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[15]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[16]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[17]  Hava T. Siegelmann,et al.  Support Vector Clustering , 2002, J. Mach. Learn. Res..

[18]  Andrei Z. Broder,et al.  Graph structure in the Web , 2000, Comput. Networks.

[19]  Andries Petrus Engelbrecht,et al.  Data clustering using particle swarm optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[20]  David G. Stork,et al.  Pattern Classification , 1973 .

[21]  Neo D. Martinez,et al.  Simple rules yield complex food webs , 2000, Nature.

[22]  Giandomenico Spezzano,et al.  Swarming agents for discovering clusters in spatial data , 2003, Second International Symposium on Parallel and Distributed Computing, 2003. Proceedings..

[23]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Nicolas Monmarché,et al.  AntClust: Ant Clustering and Web Usage Mining , 2003, GECCO.

[25]  Béla Bollobás,et al.  Random Graphs , 1985 .

[26]  Thomas E. Potok,et al.  A flocking based algorithm for document clustering analysis , 2006, J. Syst. Archit..

[27]  William S. Yamamoto,et al.  AY's Neuroanatomy of C. elegans for Computation , 1992 .

[28]  Jon M. Kleinberg,et al.  The small-world phenomenon: an algorithmic perspective , 2000, STOC '00.

[29]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[30]  Mark S. Granovetter The Strength of Weak Ties , 1973, American Journal of Sociology.

[31]  P. Erdos,et al.  On the strength of connectedness of a random graph , 1964 .

[32]  S. Redner How popular is your paper? An empirical study of the citation distribution , 1998, cond-mat/9804163.