Abstract
Expressing the expert expectations in form of boolean constraints during the data clustering process seems to be a promising issue to improve the quality of the generated clusters. However, in some real problems, an explosion in the volume of the processed data and their related constraints overwhelm the expert. In this paper, we aim to explicitly formulate the expert preferences on supervising the clustering mechanism through injecting their degree of interest on constraints using scoring functions. Therefore, we introduce our algorithm \(\mathcal{SHAQAR}\) for quantitative ranking of constraints during the data clustering. An intensive experimental evaluation, carried out on OLAP query logs collected from a financial data warehouse, showed that \(\mathcal{SHAQAR}\) outperforms the pioneer algorithms, i.e. Klein et al’s algorithm.
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
Bade, K., Hermkes, M., Nürnberger, A.: User Oriented Hierarchical Information Organization and Retrieval. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 518–526. Springer, Heidelberg (2007)
Basu, S., Banerjee, A., Mooney, R.J.: Active semi-supervision for pairwise constrained clustering. In: Proceedings of the SIAM International Conference on Data Mining (SDM 2004), pp. 333–344 (2004)
Ben Ahmed, E., Nabli, A., Gargouri, F.: Building MultiView Analyst Profile From Multidimensional Query Logs: From Consensual to Conflicting Preferences. The International Journal of Computer Science Issues (IJCSI 2012) 9(1), 124–131 (2012)
Ben Ahmed, E., Nabli, A., Gargouri, F.: Performing Groupization in Data Warehouses: Which Discriminating Criterion to Select? In: Bouma, G., Ittoo, A., Métais, E., Wortmann, H. (eds.) NLDB 2012. LNCS, vol. 7337, pp. 234–240. Springer, Heidelberg (2012)
Ben Ahmed, E., Nabli, A., Gargouri, F.: \(\mathcal{SHACUN}\): Semi-supervised Hierarchical Active Clustering Based on Ranking Constraints. In: Perner, P. (ed.) ICDM 2012. LNCS, vol. 7377, pp. 194–208. Springer, Heidelberg (2012)
Bohm, C., Plant, C.: Hissclu: A hierarchical density-based method for semi-supervised clustering. In: Proceedings of the International Conference on Extending Database Technology (EDBT 2008), New York, USA, pp. 440–451 (2008)
Davidson, I., Ravi, S.S.: Using instance-level constraints in agglomerative hierarchical clustering: theoretical and empirical results. Data Mining and Knowledge Discovery 18(2), 257–282 (2009)
Dubey, A., Bhattacharya, I., Godbole, S.: A Cluster-Level Semi-supervision Model for Interactive Clustering. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS, vol. 6321, pp. 409–424. Springer, Heidelberg (2010)
Kestler, H.A., Kraus, J.M., Palm, G., Schwenker, F.: On the Effects of Constraints in Semi-supervised Hierarchical Clustering. In: Schwenker, F., Marinai, S. (eds.) ANNPR 2006. LNCS (LNAI), vol. 4087, pp. 57–66. Springer, Heidelberg (2006)
Klein, D., Kamvar, S.D., Manning, C.D.: From instance-level constraints to space-level constraints: making the most of prior knowledge in data clustering. In: Proceedings of the International Conference on Machine Learning (ICML 2002), pp. 307–314. Springer, San Francisco (2002)
Nogueira, B.M., Jorge, A.M., Rezende, S.O.: Hierarchical confidence-based active clustering. In: Proceedings of the Symposium on Applied Computing, pp. 535–537 (2012)
Provost, F., Fawcett, T.: The case against accuracy estimation for comparing induction algorithm. In: Proceedings of the International Conference on Machine Learning, Madison, Wisconsin, USA, pp. 445–453 (1998)
Quinlan, J.R.: Induction of decision trees. Machine Learning, 81–106 (1986)
Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley, Boston (2005)
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
Ben Ahmed, E., Nabli, A., Gargouri, F. (2012). Towards Quantitative Constraints Ranking in Data Clustering. In: Liddle, S.W., Schewe, KD., Tjoa, A.M., Zhou, X. (eds) Database and Expert Systems Applications. DEXA 2012. Lecture Notes in Computer Science, vol 7447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32597-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-32597-7_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32596-0
Online ISBN: 978-3-642-32597-7
eBook Packages: Computer ScienceComputer Science (R0)