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Please use this identifier to cite or link to this item: http://hdl.handle.net/2451/27804

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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