Visualization of Similarities and Dissimilarities in Rules Using Multidimensional Scaling
Visualization of Similarities and Dissimilarities in Rules Using Multidimensional Scaling
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DOI：
10.1007/11425274_4
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发表时间：
2005-05
期刊：
影响因子：
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通讯作者：
S. Tsumoto;S. Hirano
中科院分区：
文献类型：
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作者：
S. Tsumoto;S. Hirano
规则归纳方法最重要的问题之一是领域专家很难检查从大数据集中生成的数百万条规则。这些规则的发现需要领域知识的深入解释。虽然在数据挖掘和知识发现的研究中已经提出了一些解决方案，但这些研究并没有关注所获得的规则之间的相似性。当一个规则1具有合理的特征，而另一个规则2具有高相似性或1包含意外因素时，这些规则之间的关系将成为知识发现的触发器。本文提出了一种基于多维尺度的规则相似性和不相似性的可视化方法，该方法根据数据点与其他数据点的相似性信息，为每个数据点分配一个二维坐标。我们在两个医学数据集上对该方法进行了评估，实验结果表明，该方法可以发现对领域专家有用的知识。
One of the most important problems with rule induction methods is that it is very difficult for domain experts to check millions of rules generated from large datasets. The discovery from these rules requires deep interpretation from domain knowledge. Although several solutions have been proposed in the studies on data mining and knowledge discovery, these studies are not focused on similarities between rules obtained. When one ruler1has reasonable features and the other ruler2with high similarity tor1includes unexpected factors, the relations between these rules will become a trigger to the discovery of knowledge. In this paper, we propose a visualization approach to show the similar and dissimilar relations between rules based on multidimensional scaling, which assign a two-dimensional cartesian coordinate to each data point from the information about similiaries between this data and others data. We evaluated this method on two medical data sets, whose experimental results show that knowledge useful for domain experts could be found.