Conceptual Modeling for Classification Mining in Data Warehouses
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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
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.
KeywordsData warehouses conceptual modeling multidimensional modeling data mining UML extension classification decision trees
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