A traffic-based evolutionary algorithm for network clustering

Abstract Network clustering algorithms are typically based only on the topology information of the network. In this paper, we introduce traffic as a quantity representing the intensity of the relationship among nodes in the network, regardless of their connectivity, and propose an evolutionary clustering algorithm, based on the application of genetic operators and capable of exploiting the traffic information. In a comparative evaluation based on synthetic instances and two real world datasets, we show that our approach outperforms a selection of well established evolutionary and non-evolutionary clustering algorithms.

[1]  Ramesh Govindan,et al.  An analysis of Internet inter-domain topology and route stability , 1997, Proceedings of INFOCOM '97.

[2]  Steve Uhlig,et al.  Providing public intradomain traffic matrices to the research community , 2006, CCRV.

[3]  Bodo Manthey,et al.  Smoothed Analysis of the k-Means Method , 2011, JACM.

[4]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[5]  S. Sheather.  Density Estimation , 2004 .

[6]  Andrea Vattani.  k-means Requires Exponentially Many Iterations Even in the Plane , 2011, Discret. Comput. Geom..

[7]  Maurizio Naldi,et al.  An evolutionary algorithm for network clustering through traffic matrices , 2011, 2011 7th International Wireless Communications and Mobile Computing Conference.

[8]  Charu C. Aggarwal,et al.  Graph Clustering , 2010, Encyclopedia of Machine Learning and Data Mining.

[9]  Miguel Rio,et al.  Network topologies: inference, modeling, and generation , 2008, IEEE Communications Surveys & Tutorials.

[10]  Xin Yao,et al.  Software Module Clustering as a Multi-Objective Search Problem , 2011, IEEE Transactions on Software Engineering.

[11]  M. C. Sinclair.  Improved model for European international telephony traffic , 1994 .

[12]  M. Newman.  Analysis of weighted networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Mary Inaba,et al.  Applications of weighted Voronoi diagrams and randomization to variance-based k-clustering: (extended abstract) , 1994, SCG '94.

[14]  Heinz Mühlenbein,et al.  Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization , 1993, Evolutionary Computation.

[15]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[16]  M. Bacharach.  Economics and the Theory of Games , 2019 .

[17]  Gérard Hébuterne,et al.  Communicating Systems & Networks: Traffic & Performance , 2004 .

[18]  Kwan-Liu Ma,et al.  Social Network Discovery Based on Sensitivity Analysis , 2009, 2009 International Conference on Advances in Social Network Analysis and Mining.

[19]  Alan M. Frieze,et al.  Clustering Large Graphs via the Singular Value Decomposition , 2004, Machine Learning.

[20]  Satu Elisa Schaeffer,et al.  Graph Clustering , 2017, Encyclopedia of Machine Learning and Data Mining.

[21]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[22]  Stanley M. Selkow,et al.  Algorithms in a nutshell - a desktop quick reference , 2009 .

[23]  Bernard Muschielok,et al.  The 4MOST instrument concept overview , 2014, Astronomical Telescopes and Instrumentation.

[24]  Bara'a Ali Attea,et al.  A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks , 2012, Appl. Soft Comput..

[25]  Christos Gkantsidis,et al.  Spectral analysis of Internet topologies , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[26]  Victor Y. Pan,et al.  The complexity of the matrix eigenproblem , 1999, STOC '99.

[27]  Farrukh Aslam Khan,et al.  Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization , 2012, Appl. Soft Comput..

[28]  Xin Yao,et al.  An evolutionary clustering algorithm for gene expression microarray data analysis , 2006, IEEE Transactions on Evolutionary Computation.

[29]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  Maurizio Naldi,et al.  Traffic-based network clustering , 2010, IWCMC.

[31]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[32]  Chris Ding,et al.  On the Use of Singular Value Decomposition for Text Retrieval , 2000 .

[33]  Keith Marzullo,et al.  Directional Gossip: Gossip in a Wide Area Network , 1999, EDCC.

[34]  Erick Cantú-Paz,et al.  Order statistics and selection methods of evolutionary algorithms , 2002, Inf. Process. Lett..

[35]  Maurizio Naldi,et al.  Blind maximum likelihood estimation of traffic matrices under long-range dependent traffic , 2010, Comput. Networks.

[36]  Christophe Diot,et al.  Taxonomy of IP traffic matrices , 2002, SPIE ITCom.

[37]  Alex Alves Freitas,et al.  A Survey of Evolutionary Algorithms for Clustering , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[38]  Mehmed Kantardzic.  What is Data Mining? , 2003 .

[39]  S. Dongen.  A cluster algorithm for graphs , 2000 .

[40]  Maurizio Naldi,et al.  Blind Maximum-Likelihood Estimation of Traffic Matrices in Long Range Dependent Traffic , 2009, FITraMEn.

[41]  PL Conti,et al.  Estimation of traffic matrices in the presence of long memory traffic , 2012 .