Abstract
How to increase both autonomy and versatility of a knowledge discovery system is a core problem and a crucial aspect of KDD (Knowledge Discovery in Databases). Within the framework of KDD process and the GLS (Global Learning Scheme) system recently proposed by us, this paper describes a way of increasing both autonomy and versatility of a KDD system. In our approach, the KDD process is modeled as an organized society of KDD agents with multiple levels. We propose a formalism to describe KDD agents, in the style of OOER (Object Oriented Entity Relationship data model). Based on this representation of KDD agents as operators, we apply several AI planning techniques, which are implemented as a meta-agent, so that we might (1) solve the most difficult problem in a multi-strategy and cooperative KDD system: how to automatically choose appropriate KDD techniques (KDD agents) to achieve a particular discovery goal in a particular application domain; (2) tackle the complexity of KDD process; and (3) support evolution of KDD data, knowledge, and process. The GLS system, as a multi-strategy and cooperative KDD system based on the approach and using the planning mechanism, increases both autonomy and versatility.
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Zhong, N., Liu, C., Ohsuga, S. (1997). A way of increasing both autonomy and versatility of a KDD system. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1997. Lecture Notes in Computer Science, vol 1325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63614-5_9
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DOI: https://doi.org/10.1007/3-540-63614-5_9
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