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Please use this identifier to cite or link to this item: http://hdl.handle.net/2451/14158

Title: Automated Construction of Relational Attributes ACORA: A Progress Report
Authors: Perlich, Claudia
Issue Date: Aug-2002
Publisher: Stern School of Business, New York University
Series/Report no.: IS-02-04
Abstract: Data mining research has not only development a large number of algorithms, but also enhanced our knowledge and understanding of their applicability and performance. However, the application of data mining technology in business environments is still no very common, despite the fact that organizations have access to large amounts of data and make decisions that could profit from data mining on a daily basis. One of the reasons is the mismatch between data representation for data storage and data analysis. Data are most commonly stored in multi-table relational databases whereas data mining methods require that the data be represented as a simple feature vector. This work presents a general framework for feature construction from multiple relational tables for data mining applications. The second part describes our prototype implementation ACORA (Automated Construction of Relational Features).
URI: http://hdl.handle.net/2451/14158
Appears in Collections:IOMS: Information Systems Working Papers

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