Demsar Janez, Leban Gregor, Zupan Blaz
Faculty of Computer and Information Science, University of Ljubljana, Trzaska 25, SI-1000 Ljubljana, Slovenia.
J Biomed Inform. 2007 Dec;40(6):661-71. doi: 10.1016/j.jbi.2007.03.010. Epub 2007 Apr 20.
Visualization can largely improve biomedical data analysis. It plays a crucial role in explorative data analysis and may support various data mining tasks. The paper presents FreeViz, an optimization method that finds linear projection and associated scatterplot that best separates instances of different class. In a single graph, the resulting FreeViz visualization can provide a global view of the classification problem being studied, reveal interesting relations between classes and features, uncover feature interactions, and provide information about intra-class similarities. The paper gives mathematical foundations of FreeViz, and presents its utility on various biomedical data sets.
可视化在很大程度上可以改善生物医学数据分析。它在探索性数据分析中起着至关重要的作用,并可能支持各种数据挖掘任务。本文介绍了FreeViz,这是一种优化方法,可找到能最佳分离不同类别实例的线性投影及相关散点图。在单个图表中,由此产生的FreeViz可视化可以提供所研究分类问题的全局视图,揭示类别与特征之间有趣的关系,发现特征相互作用,并提供有关类内相似性的信息。本文给出了FreeViz的数学基础,并展示了其在各种生物医学数据集上的效用。