Distributed Learning: An Agent-Based Approach to Data-Mining
نویسندگان
چکیده
This extended abstract summarises our current research which spans the fields of knowledge discovery and software agents. Knowledge discovery (or data-mining) is concerned with extracting knowledge from databases and/or knowledge bases (Piatetsky-Shapiro & Frawley, 1991) using machine learning techniques. Traditionally, data-mining systems are designed to work on a single dataset. However, with the growth of networks, data is increasingly dispersed over many machines in many different geographical locations. Also, whilst most practical data-mining algorithms operate over propositional representations, we are using first order learning algorithms (Muggleton, 1992). This is to enable us to explore the aspects of knowledge integration and theory refinement which do not appear in propositional systems. However, this paper only presents preliminary, propositional results which do not reflect the more complex aspects associated with first order learning.
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