Self-Organising Data Mining
Self-Organising Data Mining
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DOI：
10.1080/0232929031000136135
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发表时间：
2003-02
期刊：
影响因子：
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通讯作者：
F. Lemke;Jaroslav Müller
中科院分区：
文献类型：
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作者：
F. Lemke;Jaroslav Müller
在这篇文章中，描述了通过应用自组织和其他原理或多或少地自动化整个数据挖掘过程的可能性，我们称之为自组织数据挖掘。实现了不同的基于GMDH的建模算法-降维，缺失值消除，活性神经元，增强的网络合成和方程系统的创建，验证，组合替代模型-使知识提取客观，快速和易于使用，即使是大型和复杂的系统。
In the article is described the possibility to automate by means of application of self-organisation and other principles more or less the whole data mining process, what we have named self-organising data mining. There are different GMDH-based modelling algorithms implemented - dimensionality reduction, missing value elimination, active neurons, enhanced network synthesis and creation of systems of equations, validation, combining of alternative models - to make knowledge extraction objective, fast and easy-to-use even for large and complex systems.