淡江大學機構典藏:Item 987654321/98667
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    题名: Exploratory data analysis of interval-valued symbolic data with matrix visualization
    作者: Kao, Chiun-How;Nakano, Junji;Shieh, Sheau-Hue;Tien, Yin-Jing;Wu, Han-Ming;Yang, Chuan-kai;Chen, Chun-houh
    贡献者: 淡江大學數學學系
    关键词: Symbolic data analysis;Interval-valued data;Matrix visualization;Generalized association plots;Proximity matrix;Exploratory data analysis;EDA
    日期: 2014-11-01
    上传时间: 2014-09-04 18:29:56 (UTC+8)
    出版者: Amsterdam: Elsevier BV
    摘要: Symbolic data analysis (SDA) has gained popularity over the past few years because of its potential for handling data having a dependent and hierarchical nature. Amongst many methods for analyzing symbolic data, exploratory data analysis (EDA: Tukey, 1977) with graphical presentation is an important one. Recent developments of graphical and visualization tools for SDA include zoom star, closed shapes, and parallel-coordinate-plots. Other studies project high dimensional symbolic data into lower dimensional spaces using symbolic data versions of principal component analysis, multidimensional scaling, and self-organizing maps. Most graphical and visualization approaches for exploring symbolic data structure inherit the advantages of their counterparts for conventional (non-symbolic) data, but also their disadvantages. Here we introduce matrix visualization (MV) for visualizing and clustering symbolic data using interval-valued symbolic data as an example; it is by far the most popular symbolic data type in the literature and the most commonly encountered one in practice. Many MV techniques for visualizing and clustering conventional data are converted to symbolic data, and several techniques are newly developed for symbolic data. Various examples of data with simple to complex structures are brought in to illustrate the proposed methods.
    關聯: Computational Statistics & Data Analysis 79, pp.14-29
    DOI: 10.1016/j.csda.2014.04.012
    显示于类别:[數學學系暨研究所] 期刊論文

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