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We devised a data mining agent (i.e., an autonomous software tool), which implements two sampling methodologies (breadth-first search and uniform sampling).
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Data mining agents operate in a server GRID cluster.
Instead of mining frequent itemsets from customer transactions, the new algorithm discovers new neurofuzzy agents and mines agent associations in first-order logic for coordination that was once considered impossible in traditional data mining.
His first choice was Richard Smith who was the mine agent to the Trustees of the 1st Earl of Dudley.
To address this challenge, this study presents a: (i) framework for modeling the effect of information diffusion on community acceptance of mining using agent-based modeling (ABM); and (ii) case study to illustrate the framework.
This paper describes emerging electronic partnerships between players in developing electronic marketspaces and identifies typical data flows between such players, with an analysis of the potential role of data mining and intelligent agent technology.
Our work shows the techniques and methods resulting from the field of agent mining applied to many aspects including intelligent traffic control, dynamic routing, congestion management, decision support, modeling and simulation.
Unfortunately, aspect mining for multi-agent systems is an unexplored research area.
We propose, in this paper, a (semi-automatic) hybrid aspect mining approach for agent-oriented code.
We propose to attack this problem using an adaptive population of intelligent agents mining the Web online at query time.
In the MDM system, a load profiling method is being applied and a specific data-mining technique on agent grid clustering is used for the processing of the data.
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