|
Archive@NYU >
Stern School of Business >
IOMS: Information Systems Working Papers >
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
|
All items in Faculty Digital Archive are protected by copyright, with all rights reserved.
|
