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題名 Exploratory data analysis of interval-valued symbolic data with matrix visualization 作者 吳漢銘
Wu, Han-Ming
Kao, Chiun-How
Nakano, Junji
Shieh, Sheau-Hue
Tien, Yin-Jing
Yang, Chuan-kai
Chen, Chun-houh貢獻者 統計系 關鍵詞 Symbolic data analysis;Interval-valued data;Matrix visualization;Generalized association plots;Proximity matrix;Exploratory data analysis;EDA 日期 2014-11 上傳時間 2022-04-12 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 and Data Analysis, Vol.79, pp.14-29 資料類型 article DOI https://doi.org/10.1016/j.csda.2014.04.012 dc.contributor 統計系 dc.creator (作者) 吳漢銘 dc.creator (作者) Wu, Han-Ming dc.creator (作者) Kao, Chiun-How dc.creator (作者) Nakano, Junji dc.creator (作者) Shieh, Sheau-Hue dc.creator (作者) Tien, Yin-Jing dc.creator (作者) Yang, Chuan-kai dc.creator (作者) Chen, Chun-houh dc.date (日期) 2014-11 dc.date.accessioned 2022-04-12 - dc.date.available 2022-04-12 - dc.date.issued (上傳時間) 2022-04-12 - dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/139843 - dc.description.abstract (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. dc.format.extent 7356999 bytes - dc.format.mimetype application/pdf - dc.relation (關聯) Computational Statistics and Data Analysis, Vol.79, pp.14-29 dc.subject (關鍵詞) Symbolic data analysis;Interval-valued data;Matrix visualization;Generalized association plots;Proximity matrix;Exploratory data analysis;EDA dc.title (題名) Exploratory data analysis of interval-valued symbolic data with matrix visualization dc.type (資料類型) article dc.identifier.doi (DOI) 10.1016/j.csda.2014.04.012 dc.doi.uri (DOI) https://doi.org/10.1016/j.csda.2014.04.012
