Swarms on Continuous Data
45. Vitorino Ramos, Ajith
Abraham, Swarms on Continuous Data, in CEC´03 - Congress on Evolutionary Computation, IEEE Press,
ISBN 078-0378-04-0, pp.1370-1375,
Canberra, Australia, 8-12 Dec. 2003.
PDF file: paper (140 Kb)
Abstract: While being it extremely important, many Exploratory Data Analysis (EDA) systems have the inhability to perform classification and visualization in a continuous basis or to self-organize new data-items into the older ones (evenmore into new labels if necessary), which can be crucial in KDD - Knowledge Discovery, Retrieval and Data Mining Systems (interactive and online forms of Web Applications are just one example). This disadvantge is also present in more recent approaches using Self-Organizing Maps. On the present work, and exploiting past sucesses in recently proposed Stigmergic Ant Systems a robust online classifier is presented, which produces class decisions on a continuous stream data, allowing for continuous mappings. Results show that increasingly better results are achieved, as demonstraded by other authors in different areas.
º Lutz Herrmann and Alfred Ultsch, "An Artificial Life Approach for Semi-supervised Learning", in Data Analysis, Machine Learning and Applications, Book Series Studies in Classification, Data Analysis, and Knowledge Organization, pp. 139-146, Springer, 2008.
º Tom Fawcett, "Data Mining with Cellular Automata", Knowledge Discovery and Data Mining (KDD) Journal, 2007.
º Braden Box, "Ad Placement on the Internet Using Ant-Based Algorithms", Honours Project, Dep. of Computer Science, Carleton University, Ottawa, Canada, 2007.
º Bo Liu, Jiu-hui Pan, "Research of incremental Data Mining based on Swarm Intelligence", in Computer Engineering and Design Journal, Vol.27, No.11, pp. 1939-1942, 2006.
º Claus Aranha and Hitoshi Iba, "The Effect of Using Evolutionary Algorithms on Ant Clustering Techniques", Proceedings of the 2006 Asia Pacific Workshop on Genetic Programming (ASPGP06), pp. 24-34, 2006.
º David Martens, Manu de Backer, Raf Haesen, Bart Baesens, Tom Holvoet, "Ants Constructing Rule-based Classifiers", in Swarm Intelligence in Data Mining, A. Abraham, C. Grosan, V. Ramos (Eds.), Studies in Computational Intelligence (series), Vol. 34, pp. 21-43, Springer, Germany, Set. 2006.
º C. Aranha, H. Iba, "Using Genetic Algorithms to improve Ant Colony Clustering", MPS 06, Japan, 2006.
º Wang Shugen, Yang Yun, Lin Ying, Cao Chonghua, "Automatic Classification of Remotely Sensed Images Based on Artificial Ant Colony Algorithm", in Computer Engineering and Applications Journal, Vol.41, No.29, pp. 77-80, 116, 2005.
º Bo, L., Jiuhui, P., McKay, R.I., "Incremental clustering based on swarm intelligence", in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 4247, LNCS, pp. 189-196, 2006.
º Jhon Alexis Sanabria Garzón, "Minería Web utilizando Lógica Difusa: Importancia, Estado del Arte", in Seminario de Investigacion I, Maestria en Ingenieria de Sistemas y Computacion, Universidad Nacional de Colombia, 2006.
º Chi-Ho Tsang, Sam Kwong, "Ant Colony Clustering and Feature Extraction for Anomaly Intrusion Detection", in Swarm Intelligence in Data Mining, A. Abraham, C. Grosan, V. Ramos (Eds.), Studies in Computational Intelligence (series), Vol. 34, pp. 102-123, Springer, Germany, Set. 2006.
º JJ Ventrella, "Gliders and Riders: A Particle Swarm Selects for Coherent Space-Time Structures in Evolving Cellular Automata", in Stigmergic Optimization, A. Abraham, C. Grosan, V. Ramos (Eds.), Studies in Computational Intelligence (series), Vol. 31, pp. 131-154, Springer, Germany, Aug. 2006.
º D. Djordjevic, Q. Zhang, K. Chandramouli, T. Piatrik, S. Sav, J. Jose, J. Urban, G. Anadiotis, "State of the Art on User Relevance Feedback and Biologically Inspired Systems", in K-Space EU Project - Information Society Technologies (FP6-0270026), ID4.4 Report, Queen Mary Univ. of London (QMUL, UK), Dublin City Univ. (DCU, Ireland), Glasgow Univ. (GU, UK), Centrum voor Wiskunde en Informatica (WCI, Netherlands), 98 pages, 8 July 2006.
º Merloti, P.E., "Optimization Algorithms Inspired by Biological Ants and Swarm Behavior", San Diego State University, Artificial Intelligence Technical Report, CS550, San Diego, June 2004.
º Sanabria Garzón Jhon Alexis, "Inteligencia de Enjambre y Lógica Difusa Aplicada a la Minería Web: Importancia, Estado del Arte", Web Mining survey, Colombia, 2006.
º Nicolai Marquardt, "Swarm-Intelligence: Modelle und Anwendungen", Seminar: VR-Technologien, Prof. Dr. B. Fröhlich, Bauhaus-Universität Weimar, Jan. 2004.
º Nicolai Marquardt, "Intelligenz von Schwärmen: Grundlagen, Simulations-Modelle und Anwendungen", Bauhaus-Universität Weimar, Jan. 2004.
º Dylan A. Shell, "An Annotated Bibliography of Papers that make use of the word `Stigmergy´", Univ. of Southern California, USA, Dec. 2003.
PDF file: paper (140 Kb)
Abstract: While being it extremely important, many Exploratory Data Analysis (EDA) systems have the inhability to perform classification and visualization in a continuous basis or to self-organize new data-items into the older ones (evenmore into new labels if necessary), which can be crucial in KDD - Knowledge Discovery, Retrieval and Data Mining Systems (interactive and online forms of Web Applications are just one example). This disadvantge is also present in more recent approaches using Self-Organizing Maps. On the present work, and exploiting past sucesses in recently proposed Stigmergic Ant Systems a robust online classifier is presented, which produces class decisions on a continuous stream data, allowing for continuous mappings. Results show that increasingly better results are achieved, as demonstraded by other authors in different areas.
Keywords: Ant Systems, Swarm Intelligence, Stigmergy, Data-Mining, Exploratory Data Analysis, Image Retrieval, Ant-based Clustering, Data Mining, Continuous Classification, Collective Perception.
Cited by:º Lutz Herrmann and Alfred Ultsch, "An Artificial Life Approach for Semi-supervised Learning", in Data Analysis, Machine Learning and Applications, Book Series Studies in Classification, Data Analysis, and Knowledge Organization, pp. 139-146, Springer, 2008.
º Tom Fawcett, "Data Mining with Cellular Automata", Knowledge Discovery and Data Mining (KDD) Journal, 2007.
º Braden Box, "Ad Placement on the Internet Using Ant-Based Algorithms", Honours Project, Dep. of Computer Science, Carleton University, Ottawa, Canada, 2007.
º Bo Liu, Jiu-hui Pan, "Research of incremental Data Mining based on Swarm Intelligence", in Computer Engineering and Design Journal, Vol.27, No.11, pp. 1939-1942, 2006.
º Claus Aranha and Hitoshi Iba, "The Effect of Using Evolutionary Algorithms on Ant Clustering Techniques", Proceedings of the 2006 Asia Pacific Workshop on Genetic Programming (ASPGP06), pp. 24-34, 2006.
º David Martens, Manu de Backer, Raf Haesen, Bart Baesens, Tom Holvoet, "Ants Constructing Rule-based Classifiers", in Swarm Intelligence in Data Mining, A. Abraham, C. Grosan, V. Ramos (Eds.), Studies in Computational Intelligence (series), Vol. 34, pp. 21-43, Springer, Germany, Set. 2006.
º C. Aranha, H. Iba, "Using Genetic Algorithms to improve Ant Colony Clustering", MPS 06, Japan, 2006.
º Wang Shugen, Yang Yun, Lin Ying, Cao Chonghua, "Automatic Classification of Remotely Sensed Images Based on Artificial Ant Colony Algorithm", in Computer Engineering and Applications Journal, Vol.41, No.29, pp. 77-80, 116, 2005.
º Bo, L., Jiuhui, P., McKay, R.I., "Incremental clustering based on swarm intelligence", in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 4247, LNCS, pp. 189-196, 2006.
º Jhon Alexis Sanabria Garzón, "Minería Web utilizando Lógica Difusa: Importancia, Estado del Arte", in Seminario de Investigacion I, Maestria en Ingenieria de Sistemas y Computacion, Universidad Nacional de Colombia, 2006.
º Chi-Ho Tsang, Sam Kwong, "Ant Colony Clustering and Feature Extraction for Anomaly Intrusion Detection", in Swarm Intelligence in Data Mining, A. Abraham, C. Grosan, V. Ramos (Eds.), Studies in Computational Intelligence (series), Vol. 34, pp. 102-123, Springer, Germany, Set. 2006.
º JJ Ventrella, "Gliders and Riders: A Particle Swarm Selects for Coherent Space-Time Structures in Evolving Cellular Automata", in Stigmergic Optimization, A. Abraham, C. Grosan, V. Ramos (Eds.), Studies in Computational Intelligence (series), Vol. 31, pp. 131-154, Springer, Germany, Aug. 2006.
º D. Djordjevic, Q. Zhang, K. Chandramouli, T. Piatrik, S. Sav, J. Jose, J. Urban, G. Anadiotis, "State of the Art on User Relevance Feedback and Biologically Inspired Systems", in K-Space EU Project - Information Society Technologies (FP6-0270026), ID4.4 Report, Queen Mary Univ. of London (QMUL, UK), Dublin City Univ. (DCU, Ireland), Glasgow Univ. (GU, UK), Centrum voor Wiskunde en Informatica (WCI, Netherlands), 98 pages, 8 July 2006.
º Merloti, P.E., "Optimization Algorithms Inspired by Biological Ants and Swarm Behavior", San Diego State University, Artificial Intelligence Technical Report, CS550, San Diego, June 2004.
º Sanabria Garzón Jhon Alexis, "Inteligencia de Enjambre y Lógica Difusa Aplicada a la Minería Web: Importancia, Estado del Arte", Web Mining survey, Colombia, 2006.
º Nicolai Marquardt, "Swarm-Intelligence: Modelle und Anwendungen", Seminar: VR-Technologien, Prof. Dr. B. Fröhlich, Bauhaus-Universität Weimar, Jan. 2004.
º Nicolai Marquardt, "Intelligenz von Schwärmen: Grundlagen, Simulations-Modelle und Anwendungen", Bauhaus-Universität Weimar, Jan. 2004.
º Dylan A. Shell, "An Annotated Bibliography of Papers that make use of the word `Stigmergy´", Univ. of Southern California, USA, Dec. 2003.
Related
Works:
51. Evolving
a Stigmergic Self-Organized
Data-Mining.
63. Social
Cognitive Maps, Swarm
Collective Perception and Distributed Search on Dynamic Landscapes.
42. Self-Organized
Stigmergic Document
Maps: Environment as a
Mechanism for Context Learning.
62. Swarm Intelligence in Data Mining.
61. On
Self-Regulated Swarms, Societal
Memory, Speed and Dynamics.
70. Computational
Chemotaxis
in Ants and Bacteria
over Dynamic
Environments.
69. Binary
Ant Algorithm.