One key aspect of exploiting the huge amount of autonomous and heterogeneous data sources in the Internet is not only how to re- trieve, collect and integrate relevant information but to discover previ- ously unknown, implicit and valuable knowledge. In recent years several approaches to distributed data mining and knowledge discovery have been developed, but only a few of them make use of intelligent agents. This paper is intended to argue for the potential added value of using agent technology in the domain of knowledge discovery. We briefly review and classify existing approaches to agent-based distributed data mining, propose a novel approach to distributed data clustering based on density estimation, and discuss issues of its agent-oriented implementation.

Agent-Based Distributed Data Mining: The KDEC Scheme

Stefano Lodi;Gianluca Moro
2003

Abstract

One key aspect of exploiting the huge amount of autonomous and heterogeneous data sources in the Internet is not only how to re- trieve, collect and integrate relevant information but to discover previ- ously unknown, implicit and valuable knowledge. In recent years several approaches to distributed data mining and knowledge discovery have been developed, but only a few of them make use of intelligent agents. This paper is intended to argue for the potential added value of using agent technology in the domain of knowledge discovery. We briefly review and classify existing approaches to agent-based distributed data mining, propose a novel approach to distributed data clustering based on density estimation, and discuss issues of its agent-oriented implementation.
2003
Intelligent Information Agents The AgentLink Perspective
104
122
Matthias Klusch, Stefano Lodi , Gianluca Moro
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/907738
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