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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/27804
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| Title: | Towards Intelligent Assistance for a Data Mining Process:- |
| Authors: | Provost, Foster
Hill, Shawndra
Bernstein, Abraham |
| Keywords: | Data mining
data-mining process
intelligent assistants
knowledge discovery |
| Issue Date: | Apr-2005 |
| Publisher: | IEEE Computer Society |
| Citation: | IEEE Transactions on Knowledge and Data Engineering 17(4), pp. 503-518, 2005. |
| Series/Report no.: | CeDER-PP-2005-02 |
| 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. Extending notions developed for statistical
expert systems we present a prototype Intelligent Discovery Assistant
(IDA), which provides 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 the prototype to show that an IDA can indeed provide useful
enumerations and effective rankings in the context of simple
classification processes. We discuss how an IDA could be an important
tool for knowledge sharing among a team of data miners. Finally, we
illustrate the claims with a comprehensive demonstration of
cost-sensitive classification using a more involved process and data
from the 1998 KDDCUP competition. |
| URI: | http://hdl.handle.net/2451/27804 |
| Appears in Collections: | CeDER Published Papers
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