Abstract
Many data mining applications require clustering of large amount of data. Most clustering algorithms, however, do not work and efficiently when facing such kind of dataset. This paper presents an approach to boost one of the most prominent density-based algorithms, called DENCLUE. We show analytically that the method of adjusted mean approximation on the grid is not only a powerful tool to relieve the burden of heavy computation and memory usage, but also a close proximity of the original algorithm. An adjusted mean approximation based clustering algorithm called DENCLUE-M is constructed which exploits more advantages from the grid partition mechanism. Results of experiments also demonstrate promising performance of this approach.
Supported by the National Natural Science Foundation of China under Grant No. 79970092; the Natural Science Foundation of the education board of Jiangsu Province under Grant No. 02KJB520012.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, pp. 335–398. Morgan Kaufmann Publishers, San Francisco (2000)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Surveys 31(3), 264–323 (1999)
Hinneburg, A., Keim, D.A.: Optimal Gird-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering. In: Proc. Of the 25th VLDB Conf., Edinburgh, Scotland (1999)
Sheikholeslami, G., Chatterjee, S., Zhang, A.: Wave-Cluster: A Mlti-Resolution Clustering Approach for Very Large Spatial Databases. In: Proc. 24th VLDB Conf., New York, pp. 428–439 (1998)
Aggrawal, R., Gehrke, J., Raghawan, D.P.: Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. In: Proc. ACM SIGMOD Int. Conf. On Management of Data, Seattle, WA, pp. 94–105 (1998)
Wang, W., Yang, J., Muntz, R.: STING, A Statistical Information Grid Approach to Spatial Data Mining. In: Proc. 23rd VLDB Conf., Athens, Greece, pp. 186–195 (1998)
Hinneburg, A., Keim, D.A.: An efficient approach to clustering in large multimedia databases with noise. In: Proc. 1998 Int. Conf. Knowledge Discovery and Data Mining (KDD 1998), New York, pp. 58–65 (1998)
Scott, D.: Multivariate Density Estimation. Wiley and Sons, Chichester (1992)
Berchtold, S., Keim, D., Kriegel, H.P.: The X-tree: An Index Structure for High- Dimensional Data. In: Proc. Int. Conf. on Very Large Databases, pp. 28–39 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, C., Sun, Z., Song, Y. (2003). DENCLUE-M: Boosting DENCLUE Algorithm by Mean Approximation on Grids. In: Dong, G., Tang, C., Wang, W. (eds) Advances in Web-Age Information Management. WAIM 2003. Lecture Notes in Computer Science, vol 2762. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45160-0_20
Download citation
DOI: https://doi.org/10.1007/978-3-540-45160-0_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40715-7
Online ISBN: 978-3-540-45160-0
eBook Packages: Springer Book Archive