知识发现的多智能体体系结构

2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Pub Date : 2006-09-26 DOI:10.1109/SYNASC.2006.55

Daniel Pop, V. Negru, C. Sandru

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引用次数: 6

摘要

从数据库中发现知识(KDD)是一个复杂的过程，包括业务理解、数据理解、数据准备、建模、评估和部署几个阶段。对于每个阶段，都有许多可用的算法和方法，最终用户必须从中选择一个。AgentDiscover是一个基于多智能体的智能推荐系统，用于为每个阶段选择最合适的求解方法。这为新手和有经验的用户带来了附加价值

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Multi-Agent Architecture for Knowledge Discovery

Knowledge discovery from databases (KDD) is a complex process composed of several phases: business understanding, data understanding, data preparation, modeling, evaluation and deployment. For each of the phases, there are many algorithms and methods available, the end-user having to select one of them. The AgentDiscover is a multi-agent based intelligent recommendation system for selection of the most appropriate solving method for each phase. This brings added value for both novice and experienced users

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2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing

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