Active Data Mining

Rakesh Agrawal, Giuseppe Psaila

We introduce an active data mining paradigm that combines the recent work in data mining with the rich literature on active database systems. In this paradigm, data is continuously mined at a desired frequency. As rules are discovered, they are added to a rulebase, and if they already exist, the history of the statistical parameters associated with the rules is updated. When the history starts exhibiting certain trends, specified as shape queries in the user-specified triggers, the triggers are fired and appropriate actions are initiated. To be able to specify shape queries, we describe the constructs for defining shapes, and discuss how the shape predicates are used in a query construct to retrieve rules whose histories exhibit the desired trends. We describe how this query capability is integrated into a trigger system to realize an active mining system. The system presented here has been validated using two acts of customer data.

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