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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14168
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| Title: | Unexpectedness as a Measure of Interestingness in Knowledge Discovery |
| Authors: | Padmanabhan, Balaji
Tuzhilin, Alexander |
| Keywords: | Interestingness of Patterns
Unexpectedness
Beliefs
Belief-driven Rule Discovery |
| Issue Date: | 1997 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | IS-97-06 |
| Abstract: | Organizations are taking advantage of "data-mining" techniques
to leverage the vast amounts of data captured as they process routine
transactions. Data-mining is the process of discovering hidden structure
or patterns in data. However several of the pattern discovery methods in
datamining systems have the drawbacks that they discover too many
obvious or irrelevant patterns and that they do not leverage to a full
extent valuable prior domain knowledge that managers have. This research
addresses these drawbacks by developing ways to generate interesting
patterns by incorporating managers' prior knowledge in the process of
searching for patterns in data. Specifically we focus on providing
methods that generate unexpected patterns with respect to managerial
intuition by eliciting managers' beliefs about the domain and using
these beliefs to seed the search for unexpected patterns in data. Our
approach should lead to the development of decision support systems that
provide managers with more relevant patterns from data and aid in
effective decision making. |
| URI: | http://hdl.handle.net/2451/14168 |
| Appears in Collections: | IOMS: Information Systems Working Papers
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