Abstract
Nowadays many applications need to deal with evolving data streams. In this work, we propose an incremental clustering approach for the exploitation of user constraints on data streams. Conventional constraints do not make sense on streaming data, so we extend the classic notion of constraint set into a constraint stream. We propose methods for using the constraint stream as data items are forgotten or new items arrive. Also we present an on-line clustering approach for the cost-based enforcement of the constraints during cluster adaptation on evolving data streams. Our method introduces the concept of multi-clusters (m-clusters) to capture arbitrarily shaped clusters. An m-cluster consists of multiple dense overlapping regions, named s-clusters, each of which can be efficiently represented by a single point. Also it proposes the definition of outliers clusters in order to handle outliers while it provides methods to observe changes in structure of clusters as data evolves.
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
Aggarwal, C., Han, J., Wang, J., Yu, P.: A framework for clustering evolving data streams. In: Proc. of VLDB (2003)
Aggarwal, C., Han, J., Wang, J., Yu, P.: A framework for projected clustering of high dimensional data streams. In: Proc. of VLDB (2004)
Basu, S., Banerjee, A., Mooney, R.J.: Semi-supervised Clustering by Seeding. In: Proc. of ICML (2002)
Bilenko, M., Basu, S., Mooney, R.J.: Integrating constraints and metric learning in semi-supervised clustering. In: Proc. of ICML (2004)
Bilenko, M., Basu, S., Mooney, R.J.: A probabilistic framework for semi-supervised clustering. In: Proc. of KDD, p. 8 (2004)
Cao, F., Ester, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise. In: Proc. of SDM (2006)
Davidson, I., Ravi, S.S.: Agglomerative Hierarchical Clustering with Constraints: Theoretical and Empirical Results. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 59–70. Springer, Heidelberg (2005)
Davidson, I., Ravi, S.: Clustering with constraints: Feasibility issues and the k-means algorithm. In: Proc. of SDM, Newport Beach, CA (April 2005)
Davidson, I., Wagstaff, K.L., Basu, S.: Measuring Constraint-Set Utility for Partitional Clustering Algorithms. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 115–126. Springer, Heidelberg (2006)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A Density-Based Algortihm for Discovering Clusters in Large Spatial Database with Noise. In: Proc. of KDD (1996)
Halkidi, M., Gunopulos, D., Kumar, N., Vazirgiannis, M., Domeniconi, C.: A Framework for Semi-Supervised Learning Based on Subjective and Objective Clustering Criteria. In: Proc. of ICDM (2005)
Ruiz, C., Menasalvas, E., Spiliopoulou, M.: Constraint-based Clustering. In: Proc. of AWIC. SCI. Springer (2007)
Ruiz, C., Menasalvas, E., Spiliopoulou, M.: C-DenStream: Using Domain Knowledge on a Data Stream. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds.) DS 2009. LNCS, vol. 5808, pp. 287–301. Springer, Heidelberg (2009)
Ruiz, C., Spiliopoulou, M., Menasalvas, E.: User Constraints Over Data Streams. In: IWKDDS (2006)
Wagstaff, K., Cardie, C., Rogers, S., Schroedl, S.: Constrained K-means Clustering with Background Knowledge. In: Proc. of ICML (2001)
Zhang, X., Furtlehner, C., Sebag, M.: Data Streaming with Affinity Propagation. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part II. LNCS (LNAI), vol. 5212, pp. 628–643. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Halkidi, M., Spiliopoulou, M., Pavlou, A. (2012). A Semi-supervised Incremental Clustering Algorithm for Streaming Data. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30217-6_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-30217-6_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30216-9
Online ISBN: 978-3-642-30217-6
eBook Packages: Computer ScienceComputer Science (R0)