Abstract
Most correlation clustering algorithms rely on principal component analysis (PCA) as a correlation analysis tool. The correlation of each cluster is learned by applying PCA to a set of sample points. Since PCA is rather sensitive to outliers, if a small fraction of these points does not correspond to the correct correlation of the cluster, the algorithms are usually misled or even fail to detect the correct results. In this paper, we evaluate the influence of outliers on PCA and propose a general framework for increasing the robustness of PCA in order to determine the correct correlation of each cluster. We further show how our framework can be applied to PCA-based correlation clustering algorithms. A thorough experimental evaluation shows the benefit of our framework on several synthetic and real-world data sets.
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.
Similar content being viewed by others
References
Aggarwal, C.C., Yu, P.S.: Finding generalized projected clusters in high dimensional space. In: Proc.SIGMOD (2000)
Böhm, C., Kailing, K., Kröger, P., Zimek, A.: Computing clusters of correlation connected objects. In: Proc.SIGMOD (2004)
Tung, A.K.H., Xu, X., Ooi, C.B.: CURLER: Finding and visualizing nonlinear correlated clusters. In: Proc.SIGMOD (2005)
Achtert, E., Böhm, C., Kröger, P., Zimek, A.: Mining hierarchies of correlation clusters. In: Proc.SSDBM (2006)
Achtert, E., Böhm, C., Kriegel, H.P., Kröger, P., Zimek, A.: Robust, complete, and efficient correlation clustering. In: Jonker, W., Petković, M. (eds.) SDM 2007. LNCS, vol. 4721. Springer, Heidelberg (2007)
Achtert, E., Böhm, C., Kriegel, H.P., Kröger, P., Zimek, A.: On exploring complex relationships of correlation clusters. In: Proc.SSDBM (2007)
Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When is nearest neighbor meaningful? In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 217–235. Springer, Heidelberg (1998)
Hinneburg, A., Aggarwal, C.C., Keim, D.A.: What is the nearest neighbor in high dimensional spaces? In: Proc.VLDB (2000)
Aggarwal, C.C., Hinneburg, A., Keim, D.: On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973. Springer, Heidelberg (2000)
Chakrabarti, K., Mehrotra, S.: Local dimensionality reduction: A new approach to indexing high dimensional spaces. In: Proc.VLDB (2000)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc.KDD (1996)
Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: Ordering points to identify the clustering structure. In: Proc.SIGMOD (1999)
Bansal, N., Blum, A., Chawla, S.: Correlation clustering. Mach. Learn. 56, 89–113 (2004)
Liebl, B., Nennstiel-Ratzel, U., von Kries, R., Fingerhut, R., Olgemöller, B., Zapf, A., Roscher, A.A.: Very high compliance in an expanded MS-MS-based newborn screening program despite written parental consent. Preventive Medicine 34(2), 127–131 (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kriegel, HP., Kröger, P., Schubert, E., Zimek, A. (2008). A General Framework for Increasing the Robustness of PCA-Based Correlation Clustering Algorithms. In: Ludäscher, B., Mamoulis, N. (eds) Scientific and Statistical Database Management. SSDBM 2008. Lecture Notes in Computer Science, vol 5069. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69497-7_27
Download citation
DOI: https://doi.org/10.1007/978-3-540-69497-7_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69476-2
Online ISBN: 978-3-540-69497-7
eBook Packages: Computer ScienceComputer Science (R0)