Evolving a Stigmergic Self-Organized Data Mining
51. Vitorino Ramos, Ajith
Abraham, Evolving a Stigmergic Self-Organized Data-Mining, in ISDA-04,
4th Int. Conf. on Intelligent Systems, Design and Applications,
Budapest, Hungary, ISBN 963-7154-30-2, pp. 725-730, August 26-28, 2004.
Figure - Web Usage Mining of Monash's Univ. (Australia) web site using self-organized ant-based clustering (initial and final maps).
PDF file: paper (266 Kb)
Abstract: Self-organizing complex systems typically are comprised of a large number of frequently similar components or events. Through their process, a pattern at the global-level of a system emerges solely from numerous interactions among the lower-level components of the system. Moreover, the rules specifying interactions among the system’s components are executed using only local information, without reference to the global pattern, which, as in many real-world problems is not easily accessible or possible to be found. Stigmergy, a kind of indirect communication and learning by the environment found in social insects is a well know example of self-organization, providing not only vital clues in order to understand how the components can interact to produce a complex pattern, as can pinpoint simple biological non-linear rules and methods to achieve improved artificial intelligent adaptive categorization systems, critical for Data-Mining. On the present work it is our intention to show that a new type of Data-Mining can be designed based on Stigmergic paradigms, taking profit of several natural features of this phenomenon. By hybridizing bio-inspired Swarm Intelligence with Evolutionary Computation we seek for an entire distributed, adaptive, collective and cooperative self-organized Data-Mining. As a real-world / real-time test bed for our proposal, World-Wide-Web Mining will be used. Having that purpose in mind, Web usage Data was collected from the Monash University’s Web site (Australia), with over 7 million hits every week. Results are compared to other recent systems, showing that the system presented is by far promising.
º Leila Djerou, Nacer Khelil, Mohamed Batouche, "Image Segmentation by Self-Organised Region Growing", in 7th Computer Information Systems and Industrial Management Applications - CISIM 08, pp. 171-176, IEEE Press, 2008.
º Václav Snásel, Pavel Krömer and Jan Plato, "Benchmarking Hybrid Selection and Adaptive Genetic Operators", in Václav Snásel (Ed.): Znalosti 2008, pp. 224-233, ISBN 978-80-227-2827-0, Institute of Informatics and Software Engineering, FIIT STU Press, Bratislava, 2008.
º Bin Zhang, Yi-Dan Su, "An Ant Colony Clustering Algorithm Based on Directional Similarity: ACCADS", in Computer and Modernization Journal, n. 3, pp. 86-89, China, 2008.
º P.H. Van Dyke, T.C. Belding, S. Brueckner, P. Chiusano, "Hierarchical Ant Clustering and Foraging", US Patent 20,070,179,944, USA, 2007.
º N. Hoschke, C. J. Lewis, D. C. Price, D. A. Scott, V. Gerasimov and P. Wang, "A Self-organizing Sensing System for Structural Health Monitoring of Aerospace Vehicles", in Mikhail Prokopenko (Ed.), Advances in Applied Self-organizing Systems, ISBN 978-1-84628-981-1, Part II, pp. 51-76, Springer London, Nov. 2007.
º Matthias Baumgarten, Kieran Greer, Maurice Mulvenna, Kevin Curran, Chris Nugent, "Utilizing Stigmergy in Support of Autonomic Principles", in Third IEEE Conf. Conference on Semantics, Knowledge and Grid, Shan Xi, China, pp. 98-103, IEEE Press, Oct. 2007.
º Andrew Vande Moere, "A Model for Self-Organizing Data Visualization Using Decentralized Multiagent Systems", in Advances in Applied Self-Organizing Systems, Mikhail Prokopenko (Ed.), pp. 291-324, part III, Springer, Collection Computer Science, London, Nov. 2007.
º Swee Chuan Tan, Kai Ming Ting and Shyh Wei Teng, "Examining Dissimilarity Scaling in Ant Colony Approaches to Data Clustering", in Progress in Artificial Life, LNCS, Vol. 4828, pp. 269-280, Springer, Nov. 2007.
º Sergio Gutiérrez, Abelardo Pardo, Carlos Delgado Kloos, "Swarm Intelligence Applications for the Internet", in Encyclopedia of Internet Technologies and Applications, M. Freire, M. Pereira (Eds.), Information Science Reference Press, ISBN-10: 1591409934, pp. 600-605, Hershey - New York, Oct. 2007.
º Kieran Greer, Matthias Baumgarten, Maurice Mulvenna, Chris Nugent and Kevin Curran, "Knowledge-Based Reasoning Through Stigmergic Linking", in Self-Organizing Systems, LNCS, Springer, Vol. 4725, pp. 240-254, 2007.
º Bart Gilner, "A Comparative Study Of Ant Clustering Algorithms", in Msc Thesis, University of Maastricht, Department of Mathematics, Netherlands, October 2007.
º Maxim Shoshany, Asaf Even-Paz, Shlomo Bekhor, "Evolution of clusters in dynamic point patterns: with a case study of Ants' simulation", in Int. Journal of Geographical Information Science, Volume 21, Issue 7, pp. 777 - 797, Jan. 2007.
º Vande Moere A., Clayden J. J. and Dong A., "Data Clustering and Visualization using Cellular Automata Ants", ACS Australian Joint Conference on Artificial Intelligence (AI'06), LNCS/LNAI Series, Springer, Hobart, Australia, pp. 826-836, 2006.
º H. Van Dyke Parunak, Richard Rohwer, Theodore C. Belding, Sven Brueckner, "Dynamic Decentralized Any-Time Hierarchical Clustering", in 29th Annual International ACM SIGIR Conference on Research & Development on Information Retrieval, Seattle, USA, August 6-11, 2006.
º Yun Wang, Inyoung Kim, Gaston Mbateng, Shih-Yieh Ho, "A Latent Class Modeling approach to Detect Network Intrusion", in Computer Communications Journal, Vol. 30, Issue 1, pp. 93-100, 2006.
º Rafael Santos, "Princípios e Aplicações de Mineração de Dados", Ministério da Ciência e Tecnologia, Brochura do programa de Pós-Graduação em Computação Aplicada do Instituto Nacional de Pesquisas Espaciais, Brasil, 2006.
º David Kampf and Alfred Ultsch, "Swarms of Artifcial Life Forms for Clustering DNA Microarray Data", 29th Annual Conference of the German Classification Society - From Data and Information Analysis to Knowledge Engineering, University of Magdeburg, Germany, March 9-11, 2005.
º Matthias Strobbe, Vincent Verstraete, Erik Van Breusegem, Jan Coppens, Mario Pickavet, Piet Demeester, "Implementation and Evaluation of AntNet, A Distributed Shortest-Path Algorithm", in AICT/SAPIR/ELETE 05, pp. 320-325, 2005.
º Andrew Vande Moere, Justin James Clayden, "Cellular Ants: Combining Ant-Based Clustering with Cellular Automata", in 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05), pp. 177-184, 2005.
º David Kampf and Alfred Ultsch, "An Overview of Artifcial Life Approaches for Clustering", 29th Annual Conference of the German Classification Society - From Data and Information Analysis to Knowledge Engineering, University of Magdeburg, Germany, March 9-11, 2005.
º Maynard Exum, "Self-Organized Data Clustering with the help of Swarm Agents", Database Systems CS541 Class presentation (Instructor: Dr. Amin A. Abdulghani), Rutgers University, Departm. of Computer Science, New Jersey, USA, 2005.
º Donald MacDonald, Colin Fyfe, "Strategy Selection in Games Using Co-evolution Between Artificial Immune Systems", in Entertainment Computing – ICEC 2004: Third International Conference, VII Intelligent Games, Eindhoven, The Netherlands, Springer Verlag, LNCS, Vol. 3166, pp. 445-450, ISBN: 3-540 -22947-7, Sept., 2004.
º Janina A. Jakubczyc, "The Ant Colony Algorithms in Taxonomy Tasks", in Organization Support Systems 04, Ustron, Poland 2004.
º Ultsch, Alfred, "Strategies for an Artificial Life System to cluster high dimensional Data", In 6th German Workshop on Artificial Life 2004 - Abstracting and Synthesizing the Principles of Living Systems, GWAL-6, Bamberg, Germany, pp. 128-137, 14-16, 2004.
º Kun Liu, Jessica Ryan, Hillol Kargupta, "Distributed Data Mining Bibliography", Comp. Science and Electrical Eng. Department, Univ. of Maryland, USA, 2004.
Figure - Web Usage Mining of Monash's Univ. (Australia) web site using self-organized ant-based clustering (initial and final maps).
PDF file: paper (266 Kb)
Abstract: Self-organizing complex systems typically are comprised of a large number of frequently similar components or events. Through their process, a pattern at the global-level of a system emerges solely from numerous interactions among the lower-level components of the system. Moreover, the rules specifying interactions among the system’s components are executed using only local information, without reference to the global pattern, which, as in many real-world problems is not easily accessible or possible to be found. Stigmergy, a kind of indirect communication and learning by the environment found in social insects is a well know example of self-organization, providing not only vital clues in order to understand how the components can interact to produce a complex pattern, as can pinpoint simple biological non-linear rules and methods to achieve improved artificial intelligent adaptive categorization systems, critical for Data-Mining. On the present work it is our intention to show that a new type of Data-Mining can be designed based on Stigmergic paradigms, taking profit of several natural features of this phenomenon. By hybridizing bio-inspired Swarm Intelligence with Evolutionary Computation we seek for an entire distributed, adaptive, collective and cooperative self-organized Data-Mining. As a real-world / real-time test bed for our proposal, World-Wide-Web Mining will be used. Having that purpose in mind, Web usage Data was collected from the Monash University’s Web site (Australia), with over 7 million hits every week. Results are compared to other recent systems, showing that the system presented is by far promising.
Keywords: Self-organization, Stigmergy, Data-Mining, Collaborative Sequencing, Web usage mining, Linear Genetic Programming, Distributed and Collaborative Filtering, Ant-based Clustering.
Cited by:º Leila Djerou, Nacer Khelil, Mohamed Batouche, "Image Segmentation by Self-Organised Region Growing", in 7th Computer Information Systems and Industrial Management Applications - CISIM 08, pp. 171-176, IEEE Press, 2008.
º Václav Snásel, Pavel Krömer and Jan Plato, "Benchmarking Hybrid Selection and Adaptive Genetic Operators", in Václav Snásel (Ed.): Znalosti 2008, pp. 224-233, ISBN 978-80-227-2827-0, Institute of Informatics and Software Engineering, FIIT STU Press, Bratislava, 2008.
º Bin Zhang, Yi-Dan Su, "An Ant Colony Clustering Algorithm Based on Directional Similarity: ACCADS", in Computer and Modernization Journal, n. 3, pp. 86-89, China, 2008.
º P.H. Van Dyke, T.C. Belding, S. Brueckner, P. Chiusano, "Hierarchical Ant Clustering and Foraging", US Patent 20,070,179,944, USA, 2007.
º N. Hoschke, C. J. Lewis, D. C. Price, D. A. Scott, V. Gerasimov and P. Wang, "A Self-organizing Sensing System for Structural Health Monitoring of Aerospace Vehicles", in Mikhail Prokopenko (Ed.), Advances in Applied Self-organizing Systems, ISBN 978-1-84628-981-1, Part II, pp. 51-76, Springer London, Nov. 2007.
º Matthias Baumgarten, Kieran Greer, Maurice Mulvenna, Kevin Curran, Chris Nugent, "Utilizing Stigmergy in Support of Autonomic Principles", in Third IEEE Conf. Conference on Semantics, Knowledge and Grid, Shan Xi, China, pp. 98-103, IEEE Press, Oct. 2007.
º Andrew Vande Moere, "A Model for Self-Organizing Data Visualization Using Decentralized Multiagent Systems", in Advances in Applied Self-Organizing Systems, Mikhail Prokopenko (Ed.), pp. 291-324, part III, Springer, Collection Computer Science, London, Nov. 2007.
º Swee Chuan Tan, Kai Ming Ting and Shyh Wei Teng, "Examining Dissimilarity Scaling in Ant Colony Approaches to Data Clustering", in Progress in Artificial Life, LNCS, Vol. 4828, pp. 269-280, Springer, Nov. 2007.
º Sergio Gutiérrez, Abelardo Pardo, Carlos Delgado Kloos, "Swarm Intelligence Applications for the Internet", in Encyclopedia of Internet Technologies and Applications, M. Freire, M. Pereira (Eds.), Information Science Reference Press, ISBN-10: 1591409934, pp. 600-605, Hershey - New York, Oct. 2007.
º Kieran Greer, Matthias Baumgarten, Maurice Mulvenna, Chris Nugent and Kevin Curran, "Knowledge-Based Reasoning Through Stigmergic Linking", in Self-Organizing Systems, LNCS, Springer, Vol. 4725, pp. 240-254, 2007.
º Bart Gilner, "A Comparative Study Of Ant Clustering Algorithms", in Msc Thesis, University of Maastricht, Department of Mathematics, Netherlands, October 2007.
º Maxim Shoshany, Asaf Even-Paz, Shlomo Bekhor, "Evolution of clusters in dynamic point patterns: with a case study of Ants' simulation", in Int. Journal of Geographical Information Science, Volume 21, Issue 7, pp. 777 - 797, Jan. 2007.
º Vande Moere A., Clayden J. J. and Dong A., "Data Clustering and Visualization using Cellular Automata Ants", ACS Australian Joint Conference on Artificial Intelligence (AI'06), LNCS/LNAI Series, Springer, Hobart, Australia, pp. 826-836, 2006.
º H. Van Dyke Parunak, Richard Rohwer, Theodore C. Belding, Sven Brueckner, "Dynamic Decentralized Any-Time Hierarchical Clustering", in 29th Annual International ACM SIGIR Conference on Research & Development on Information Retrieval, Seattle, USA, August 6-11, 2006.
º Yun Wang, Inyoung Kim, Gaston Mbateng, Shih-Yieh Ho, "A Latent Class Modeling approach to Detect Network Intrusion", in Computer Communications Journal, Vol. 30, Issue 1, pp. 93-100, 2006.
º Rafael Santos, "Princípios e Aplicações de Mineração de Dados", Ministério da Ciência e Tecnologia, Brochura do programa de Pós-Graduação em Computação Aplicada do Instituto Nacional de Pesquisas Espaciais, Brasil, 2006.
º David Kampf and Alfred Ultsch, "Swarms of Artifcial Life Forms for Clustering DNA Microarray Data", 29th Annual Conference of the German Classification Society - From Data and Information Analysis to Knowledge Engineering, University of Magdeburg, Germany, March 9-11, 2005.
º Matthias Strobbe, Vincent Verstraete, Erik Van Breusegem, Jan Coppens, Mario Pickavet, Piet Demeester, "Implementation and Evaluation of AntNet, A Distributed Shortest-Path Algorithm", in AICT/SAPIR/ELETE 05, pp. 320-325, 2005.
º Andrew Vande Moere, Justin James Clayden, "Cellular Ants: Combining Ant-Based Clustering with Cellular Automata", in 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05), pp. 177-184, 2005.
º David Kampf and Alfred Ultsch, "An Overview of Artifcial Life Approaches for Clustering", 29th Annual Conference of the German Classification Society - From Data and Information Analysis to Knowledge Engineering, University of Magdeburg, Germany, March 9-11, 2005.
º Maynard Exum, "Self-Organized Data Clustering with the help of Swarm Agents", Database Systems CS541 Class presentation (Instructor: Dr. Amin A. Abdulghani), Rutgers University, Departm. of Computer Science, New Jersey, USA, 2005.
º Donald MacDonald, Colin Fyfe, "Strategy Selection in Games Using Co-evolution Between Artificial Immune Systems", in Entertainment Computing – ICEC 2004: Third International Conference, VII Intelligent Games, Eindhoven, The Netherlands, Springer Verlag, LNCS, Vol. 3166, pp. 445-450, ISBN: 3-540 -22947-7, Sept., 2004.
º Janina A. Jakubczyc, "The Ant Colony Algorithms in Taxonomy Tasks", in Organization Support Systems 04, Ustron, Poland 2004.
º Ultsch, Alfred, "Strategies for an Artificial Life System to cluster high dimensional Data", In 6th German Workshop on Artificial Life 2004 - Abstracting and Synthesizing the Principles of Living Systems, GWAL-6, Bamberg, Germany, pp. 128-137, 14-16, 2004.
º Kun Liu, Jessica Ryan, Hillol Kargupta, "Distributed Data Mining Bibliography", Comp. Science and Electrical Eng. Department, Univ. of Maryland, USA, 2004.
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