Abstract
In this work, we present a clustering algorithm to find clusters of different sizes, shapes and densities, to deal with overlapping cluster distributions and background noise. The algorithm is divided in two stages. In a first step, local density is estimated at each data point. In a second stage, a hierarchical approach is used by merging clusters according to the introduced cluster distance, based on heuristic measures about how modes overlap in a distribution. Experimental results on synthetic and real databases show the validity of the method.
This work has been partially supported by projects ESP2005-07724-C05-05 and TIC2003-08496 from the Spanish CICYT.
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
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. of the second International Conference on Knowledge Discovery and Data Mining, Portland, pp. 226–231 (1996)
Ertöz, L., Steinbach, M., Kumar, V.: Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data. In: Proceedings of Third SIAM International Conference on Data Mining (2003)
Figueiredo, M., Jain, A.K.: Unsupervised Learning of Mixture Models. IEEE Trans. On PAMI 24(3), 381–396 (2002)
Fred, A.L., Leitao, J.: A New Cluster Isolation Criterion Based on Dissimilarity Increments. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(8), 944–958 (2003)
Guha, S., Rastogi, R., Shim, K.: CURE: An Efficient Clustering Algorithm for Large Databases. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 73–84. ACM, New York (1998)
Hinneburg, A., Keim, D.A.: An efficient Approach to Clustering in Large Multimedia Databases with Noise. In: Proc. of the ACM SIGKDD (1998)
Karypis, G., Han, E.H., Kumar, V.: Chameleon: A Hierarchical Clustering Algorithm Using Dynamic Modeling. the IEEE Computer Society 32(8), 68–75 (1999)
Tran, T.N., Wehrens, R., Buydens, L.M.C.: Knn Density-Based Clustering for High Dimensional Multispectral Images. Analytica Chimica Acta 490, 303–312 (2003)
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
Pascual, D., Pla, F., Sánchez, J.S. (2006). Non Parametric Local Density-Based Clustering for Multimodal Overlapping Distributions. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_81
Download citation
DOI: https://doi.org/10.1007/11875581_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
eBook Packages: Computer ScienceComputer Science (R0)