Multi-Agent Systems for Intelligent Clustering

Authors: Jung-Eun Park, Kyung-Whan Oh

Abstract:

Intelligent systems are required in order to quickly and accurately analyze enormous quantities of data in the Internet environment. In intelligent systems, information extracting processes can be divided into supervised learning and unsupervised learning. This paper investigates intelligent clustering by unsupervised learning. Intelligent clustering is the clustering system which determines the clustering model for data analysis and evaluates results by itself. This system can make a clustering model more rapidly, objectively and accurately than an analyzer. The methodology for the automatic clustering intelligent system is a multi-agent system that comprises a clustering agent and a cluster performance evaluation agent. An agent exchanges information about clusters with another agent and the system determines the optimal cluster number through this information. Experiments using data sets in the UCI Machine Repository are performed in order to prove the validity of the system.

Keywords: Intelligent Clustering, Multi-Agent System, PCA, SOM, VC(Variance Criterion)

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1076836

References:

[1] M. Ankerst, M. Breunig, H. P. Kriegel, J. Sander, "Optics: Ordering points to identify the clustering structure" In Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD'99), 1999, pp. 49-60.
[2] http://www.ics.uci.edu/~mlearn/MLRepository.html
[3] Lindsay I Smith, "A tutorial on Principal Components Analysis", February, 2002 (Online) Available: http://www.cs.otago.ac.nz/cosc453/ student_tutorials/principal_components.pdf
[4] I.T. Jolliffe, 2002, Principal component analysis 2nd ed., Springer
[5] A. R. Johnson, D. W. Wichern, Applied Multivariate Statistical Analysis, 356~357, 1992, Prentice Hall,
[6] M. Emre Celebi, Y. Alp Aslandogan, "Content Based Image Retrieval Incorporating Models of Human Perception", in IEEE International Conference on Information Technology, Coding and Computing, Las Vegas, NV, April 2004.
[7] T. Kohonen, Self-Organizing Maps, 1995, Springer
[8] J. Vesanto, J. Himberg, " SOM Toolbox For Matlab 5 ", Espoo 2000, 2000
[9] S. H. Jun, J. Yang, K. W. Oh, "Automatic Determination of Cluster Size Using Machine Learning Algorithms", SSGRR 2002, 2002
[10] J. Friedman, "On bias, variance, 0/1-loss and the curse of dimensionality", Data Mining and Knowledge Discovery, 1:55-77, 1997.
[11] J. Hollmén, "Process Modeling Using the Self-Organizing Map", 1996, (Online) Available: http://citeseer.ist.psu.edu/458515.html
[12] G. Karypis, H. Han, V. Kumar, "Chameleon: A hierarchical clustering algorithm using dynamic modeling", IEEE Computer, 32(8):68-75, August 1999
[13] J.E. Park, S.H. Jun, K.W. Oh., " Intelligent Data Mining Agents for Automatic Clustering", in Conf. KIISS2002 Conf., Seoul(Korea), 2002, pp. 370-376