Human Involvement and Interactivity of the Next Generation's Data Mining Tools Mihael Ankerst   View Paper (PDF)   Return to Position Papers Abstract The basic task of the knowledge discovery and data mining (KDD) process is to extract knowledge from data such that the resulting knowledge (pattern) is useful in a given application. Obviously, only the user can determine whether the resulting knowledge satisfies this requirement. Moreover, what one user may find useful is not necessarily useful to another user. Instead of allowing an automated data mining process to iterate in a trial-and-error manner, a natural but neglected way to enhance the process is to support human involvement. To achieve the goal that the user steers and monitors the information flow without burdening him performing tasks that can be done automatically, an interface for human involvement has to be well designed and integrated in the KDD process. As additional benefits from this approach, the user better understands and trusts the resulting patterns. Visual Classification which is a recently introduced approach has shown the benefits of this new direction for decision tree classifiers. DiSC'02 © 2003 Association for Computing Machinery