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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/139843


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/139843


    Title: Exploratory data analysis of interval-valued symbolic data with matrix visualization
    Authors: 吳漢銘
    Wu, Han-Ming
    Kao, Chiun-How
    Nakano, Junji
    Shieh, Sheau-Hue
    Tien, Yin-Jing
    Yang, Chuan-kai
    Chen, Chun-houh
    Contributors: 統計系
    Keywords: Symbolic data analysis;Interval-valued data;Matrix visualization;Generalized association plots;Proximity matrix;Exploratory data analysis;EDA
    Date: 2014-11
    Issue Date: 2022-04-12
    Abstract: 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.
    Relation: Computational Statistics and Data Analysis, Vol.79, pp.14-29
    Data Type: article
    DOI 連結: https://doi.org/10.1016/j.csda.2014.04.012
    DOI: 10.1016/j.csda.2014.04.012
    Appears in Collections:[統計學系] 期刊論文

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