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Incremental Clustering Using a Core-Based Approach

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Computer and Information Sciences - ISCIS 2005 (ISCIS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3733))

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Abstract

Clustering is a division of data into groups of similar objects, with respect to a set of relevant attributes (features) of the analyzed objects. Classical partitioning clustering methods, such as k-means algorithm, start with a known set of objects, and all features are considered simultaneously when calculating objects’ similarity. But there are numerous applications where an object set already clustered with respect to an initial set of attributes is altered by the addition of new features. Consequently, a re-clustering is required. We propose in this paper an incremental, k-means based clustering method, Core Based Incremental Clustering (CBIC), that is capable to re-partition the objects set, when the attribute set increases. The method starts from the partitioning into clusters that was established by applying k-means or CBIC before the attribute set changed. The result is reached more efficiently than running k-means again from the scratch on the feature-extended object set. Experiments proving the method’s efficiency are also reported.

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References

  1. 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)

    Google Scholar 

  2. CorMac Technologies Inc, Canada: Discover the Patterns in Your Data, http://www.cormactech.com/neunet

  3. Demiroz, G., Govenir, H.A., Ilter, N.: Learning Differential Diagnosis of Eryhemato-Squamous Diseases using Voting Feature Intervals. Artificial Intelligence in Medicine

    Google Scholar 

  4. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2001)

    Google Scholar 

  5. Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs (1998)

    Google Scholar 

  6. Jain, A., Murty, M.N., Flynn, P.: Data clustering: A review. ACM Computing Surveys 31(3), 264–323 (1999)

    Article  Google Scholar 

  7. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)

    Google Scholar 

  8. Şerban, G.: A Programming Interface for Non-Hierarchical Clustering. Studia Universitatis “Babeş-Bolyai”, Informatica XLX(1) (to appear)

    Google Scholar 

  9. Şerban, G., Câmpan, A.: Core Based Incremental Clustering. Studia Universitatis “Babeş-Bolyai”, Informatica XLXI(2) (to appear)

    Google Scholar 

  10. 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., vol. 87, pp. 9193–9196 (1990)

    Google Scholar 

  11. 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)

    Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Şerban, G., Câmpan, A. (2005). Incremental Clustering Using a Core-Based Approach. In: Yolum, p., Güngör, T., Gürgen, F., Özturan, C. (eds) Computer and Information Sciences - ISCIS 2005. ISCIS 2005. Lecture Notes in Computer Science, vol 3733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569596_87

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  • DOI: https://doi.org/10.1007/11569596_87

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29414-6

  • Online ISBN: 978-3-540-32085-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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