4.7 Article

EMADS: An extendible multi-agent data miner

期刊

KNOWLEDGE-BASED SYSTEMS
卷 22, 期 7, 页码 523-528

出版社

ELSEVIER
DOI: 10.1016/j.knosys.2008.10.009

关键词

Multi-agent data mining (MADM); Classifier generation

向作者/读者索取更多资源

In this paper, we describe EMADS, an extendible multi-agent data mining system. The EMADS vision is that of a community of data mining agents, contributed by many individuals, interacting under decentralised control to address data mining requests. EMADS is seen both as an end user application and a research tool. This paper details the EMADS vision, the associated conceptual framework and the current implementation. Although EMADS may be applied to many data mining tasks; the study described here, for the sake of brevity, concentrates on agent based data classification. A full description of EMADS is presented. (C) 2009 Elsevier B.V. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据