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Ranked Centroid Projection: A Data Visualization Approach With Self-Organizing Maps

机译:排序质心投影:一种具有自组织图的数据可视化方法

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The self-organizing map (SOM) is an efficient tool for visualizing high-dimensional data. In this paper, the clustering and visualization capabilities of the SOM, especially in the analysis of textual data, i.e., document collections, are reviewed and further developed. A novel clustering and visualization approach based on the SOM is proposed for the task of text mining. The proposed approach first transforms the document space into a multidimensional vector space by means of document encoding. Afterwards, a growing hierarchical SOM (GHSOM) is trained and used as a baseline structure to automatically produce maps with various levels of detail. Following the GHSOM training, the new projection method, namely the ranked centroid projection (RCP), is applied to project the input vectors to a hierarchy of 2-D output maps. The RCP is used as a data analysis tool as well as a direct interface to the data. In a set of simulations, the proposed approach is applied to an illustrative data set and two real-world scientific document collections to demonstrate its applicability.
机译:自组织图(SOM)是用于可视化高维数据的有效工具。在本文中,SOM的聚类和可视化功能,特别是在文本数据分析(即文档收集)中的功能得到了审查和进一步开发。针对文本挖掘的任务,提出了一种基于SOM的新型聚类和可视化方法。所提出的方法首先通过文档编码将文档空间转换为多维向量空间。此后,不断发展的分层SOM(GHSOM)被训练并用作基线结构,以自动生成具有各种详细程度的地图。在GHSOM培训之后,新的投影方法,即排序的质心投影(RCP),用于将输入向量投影到二维输出图的层次结构中。 RCP用作数据分析工具以及与数据的直接接口。在一组模拟中,将所提出的方法应用于说明性数据集和两个真实世界的科学文献集,以证明其适用性。

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