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|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).|
|Appears in Collections:||IOMS: Information Systems Working Papers|
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