|
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/2451/14146
|
| Title: | Intelligent Assistance for the Data Mining Process: An Ontology-based Approach |
| Authors: | Bernstein, Abraham
Hill, Shawndra
Provost, Foster |
| Keywords: | Data mining
data-mining process
intelligent assistants
knowledge discovery |
| Issue Date: | 2002 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | IS-02-02 |
| Abstract: | A data mining (DM) process involves multiple stages. A simple, but
typical, process might include preprocessing data, applying a
data-mining algorithm, and postprocessing the mining results. There are
many possible choices for each stage, and only some combinations are
valid. Because of the large space and non-trivial interactions, both
novices and data-mining specialists need assistance in composing and
selecting DM processes. We present the concept of Intelligent Discovery
Assistants (IDAs), which provide users with (i) systematic enumerations
of valid DM processes, in order that important, potentially fruitful
options are not overlooked, and (ii) effective rankings of these valid
processes by different criteria, to facilitate the choice of DM
processes to execute. We use a prototype to show that an IDA can indeed
provide useful enumerations and effective rankings. We discuss how an
IDA is an important tool for knowledge sharing among a team of data
miners. Finally, we illustrate all the claims with a comprehensive
demonstration using a more involved process and data from the 1998
KDDCUP competition. |
| URI: | http://hdl.handle.net/2451/14146 |
| Appears in Collections: | IOMS: Information Systems Working Papers
|
All items in Faculty Digital Archive are protected by copyright, with all rights reserved.
|
