Planning tasks for knowledge discovery in databases; Performing task-oriented user-guidance
- Univ. of Karlsruhe (Germany)
Performing the complex task of Knowledge Discovery in Databases (KDD) requires a break-down of the task-complexity to enable the possibility of performing the KDD-task. Since even more techniques will appear in the future that can solve a variety of KDD-problems, a domain expert that wants to analyze his domain should have the means to work with tools that integrate several of these techniques as well as the techniques themselves. In this paper a framework is proposed for a strategy component that is to be used for a KDD-system that can guide users in breaking down the complexity of a typical KDD-task and supports him in selecting and using several ML-techniques. The goals of such a guidance component are reuse of (predefined) taskcomponents in order to decrease development time and to simplify the process of decomposing a KDD-task, task-oriented planning in order to break down complexity of a typical KDD-task and supporting post-processing (evaluation) of KDD-processes.
- OSTI ID:
- 421274
- Report Number(s):
- CONF-960830-; TRN: 96:005928-0029
- Resource Relation:
- Conference: 2. international conference on knowledge discovery and data mining, Portland, OR (United States), 2-4 Aug 1996; Other Information: PBD: 1996; Related Information: Is Part Of Proceedings of the second international conference on knowledge discovery & data mining; Simoudis, E.; Han, J.; Fayyad, U. [eds.]; PB: 405 p.
- Country of Publication:
- United States
- Language:
- English
Similar Records
Data Locality Enhancement of Dynamic Simulations for Exascale Computing (Final Report)
Materials for Advanced Ultrasupercritical Steam Turbines Task 4: Cast Superalloy Development