Abstract
This paper proposes an adaptive clustering approach. We focus on re-clustering an object set, previously clustered, when the feature set characterizing the objects increases. We have developed adaptive extensions for two traditional clustering algorithms (k-means and Hierarchical Agglomerative Clustering). These extensions can be used for adjusting a clustering, that was established by applying the corresponding non-adaptive clustering algorithm before the feature set changed. We aim to reach the result more efficiently than applying the corresponding non-adaptive algorithm starting from the current clustering or from scratch. Experiments testing the method’s efficiency are also reported.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aeberhard, S., Coomans, D., de Vel, O.: THE CLASSIFICATION PERFORMANCE OF RDA. Tech. Rep. 92–01, Dept. of Computer Science and Dept. of Mathematics and Statistics, James Cook University of North Queensland (1992)
Campan, A., Darabant, A.S., Serban, G.: Clustering Techniques for Adaptive Horizontal Fragmentation in Object Oriented Databases. In: Proceedings of the International Conference on Theory and Applications of Mathematics and Informatics ICTAMI 2005, Alba-Iulia, Romania (to appear, 2005)
CorMac Technologies Inc., Canada: Discover the Patterns in Your Data (2006), http://www.cormactech.com/neunet
Darabant, A.S., Campan, A.: Semi-supervised learning techniques: k-means clustering in OODB Fragmentation. In: pRoceedings of IEEE International Conference on Computational Cybernetics ICCC 2004, Vienna University of Technology, Austria, August 30 - September 1, pp. 333–338 (2004)
Darabant, A.S., Campan, A., Hierarchical, A.I.: Clustering for Horizontal Object Fragmentation. In: Proceedings of Int. Conf. of Computers and Communications, Oradea, May 2004, pp. 117–122 (2004)
Darabant, A.S., Campan, A.: AI Clustering Techniques: a New Approach to Object Oriented Database Fragmentation. In: Proceedings of the 8th IEEE International Conference on Intelligent Engineering Systems, Cluj Napoca, pp. 73–78 (2004)
Darabant, A.S., Campan, A., Cret, O.: Hierarchical Clustering in Object Oriented Data Models with Complex Class Relationships. In: Proceedings of the 8th IEEE International Conference on Intelligent Engineering Systems, Cluj Napoca, pp. 307–312 (2004)
Demiroz, G., Govenir, H.A., Ilter, N.: Learning Differential Diagnosis of Eryhemato-Squamous Diseases using Voting Feature Intervals. Artificial Intelligence in Medicine
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2001)
Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1998)
Jain, A., Murty, M.N., Flynn, P.: Data clustering: A review. ACM Computing Surveys 31(3), 264–323 (1999)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Şerban, G., Câmpan, A.: Core Based Incremental Clustering, Studia Universitatis. Babeş-Bolyai, Informatica L(1), 89–96 (2005)
Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. ch. 8,9, Addison Wesley, Reading (2005)
Wolberg, W., Mangasarian, O.L.: Multisurface method of pattern separation for medical diagnosis applied to breast cytology. In: Proceedings of the National Academy of Sciences, U.S.A, December 1990, vol. 87, pp. 193–9196 (1990)
Wu, F., Gardarin, G.: Gradual Clustering Algorithms. In: Proceedings of the 7th International Conference on Database Systems for Advanced Applications (DASFAA 2001), pp. 48–57 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Câmpan, A., Şerban, G. (2006). Adaptive Clustering Algorithms. In: Lamontagne, L., Marchand, M. (eds) Advances in Artificial Intelligence. Canadian AI 2006. Lecture Notes in Computer Science(), vol 4013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11766247_35
Download citation
DOI: https://doi.org/10.1007/11766247_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34628-9
Online ISBN: 978-3-540-34630-2
eBook Packages: Computer ScienceComputer Science (R0)