alexa Conceptual Modeling for Classification Mining in Data Warehouses
Engineering

Engineering

Journal of Information Technology & Software Engineering

Author(s): Jose Zubcoff, Juan Trujillo

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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. Keywords Data warehouses conceptual modeling multidimensional modeling data mining UML extension classification decision trees Data Warehousing and Knowledge Discovery Data Warehousing and Knowledge Discovery Look Inside Chapter Metrics Readers 1 Downloads 313 Provided by Bookmetrix Reference tools Export citation Add to Papers Other actions About this Book Reprints and Permissions Share Share this content on Facebook Share this content on Twitter Share this content on LinkedIn

This article was published in Data Warehousing and Knowledge Discovery and referenced in Journal of Information Technology & Software Engineering

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