Abstract
Classification is a data mining (DM) technique that generates classes allowing to predict and describe the behavior of a variable based on the characteristics of a dataset. Frequently, DM analysts need to classify large amounts of data using many attributes. Thus, data warehouses (DW) can play an important role in the DM process, because they can easily manage huge quantities of data. There are two approaches used to model mining techniques: the Common Warehouse Model (CWM) and the Predictive Model Markup Language (PMML), both focused on metadata interchanging and sharing, respectively. These standards do not take advantage of the underlying semantic rich multidimensional (MD) model which could save development time and cost. In this paper, we present a conceptual model for Classification and a UML profile that allows the design of Classification on MD models. Our goal is to facilitate the design of these mining models in a DW context by employing an expressive conceptual model that can be used on top of a MD model. Finally, using the designed profile, we implement a case study in a standard database system and show the results.
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References
Luján-Mora, S., Trujillo, J., Song, I.: A UML profile for multidimensional modeling in data warehouses. In: Data & Knowledge Engineering (DKE), May 2006 (in press)
Günzel, H., Albrecht, J., Lehner, W.: Data Mining in a Multidimensional Environment. In: Eder, J., Rozman, I., Welzer, T. (eds.) ADBIS 1999. LNCS, vol. 1691, pp. 191–204. Springer, Heidelberg (1999)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Chapman & Hall. Wadsworth, Inc., Boca Raton (1984)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Zubcoff, J., Trujillo, J.: Extending the UML for Designing Association Rule Mining Models for Data Warehouses. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2005. LNCS, vol. 3589, pp. 11–21. Springer, Heidelberg (2005)
Shang, X., Sattler, K.: Processing Sequential Patterns in Relational Databases. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2005. LNCS, vol. 3589, pp. 438–447. Springer, Heidelberg (2005)
Rizzi, S.: UML-Based Conceptual Modeling of Pattern-Bases. In: Proc. 1st Int. Workshop on Pattern Representation and Management (PaRMa 2004), March 2004, Crete, Greece (2004)
Imielinski, T., Virmani, A.: MSQL: A Query Language for Database Mining. Data Mining and Knowledge Discovery 3, 373–408 (1999)
Han, J., Fu, J., Wang, W., Koperski, K., Zaiane, O.: DMQL: A Data Mining Query Language for Relational Databases. In: DMKD 1996, Montreal, Canada (1996)
Warmer, J., Kleppe, A.: The Object Constraint Language Second Edition. Getting Your Models Ready for MDA. Addison Wesley, Reading (2003)
OMG, Object Management Group. UML Infrastructure Specification, v2.0 (October 2004), Internet: www.omg.org/cgi-bin/doc?ptc/2004-10-14
OMG: CWM Common Warehouse Metamodel Specification, http://www.omg.org
DMG, Data Mining Group. PMML Specification, v3.0, www.dmg.org/pmml-v3-0.html
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Zubcoff, J., Trujillo, J. (2006). Conceptual Modeling for Classification Mining in Data Warehouses. In: Tjoa, A.M., Trujillo, J. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2006. Lecture Notes in Computer Science, vol 4081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11823728_54
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DOI: https://doi.org/10.1007/11823728_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37736-8
Online ISBN: 978-3-540-37737-5
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