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The common approach to exploit mining constraints is to push them deeply into the mining algorithms. In our paper we argue that this approach is based on an understanding of KDD that is no longer up-to-date. In fact, today KDD is seen as a human centered, highly interactive and iterative process. Blindly enforcing constraints already during the mining runs neglects the process character of KDD and therefore is no longer state of the art. Constraints can make a single algorithm run faster but in fact we are still far from response times that would allow true interactivity in KDD. In addition we pay the price of repeated mining runs and moreover risk reducing data mining to some kind of hypothesis testing. Taking all the above into consideration we propose to do exactly the contrary of constrained mining: We accept an initial (nearly) unconstrained and costly mining run. But instead of a sequence of subsequent and still expensive constrained mining runs we answer all further mining queries from this initial result set. Whereas this is straight forward for constraints that can be implemented as filters on the result set, things get more complicated when we restrict the underlying mini

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Author

Jochen Hipp; Ulrich Güntzer

Publisher

ACM

Published

2002

Language

EN

Field

Computer Science (Physical Sciences)