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Similarities and dissimilarities of ...

Li, Na.

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Similarities and dissimilarities of landscape artists.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Similarities and dissimilarities of landscape artists./
作者:	Li, Na.
面頁冊數:	137 p.
附註:	Source: Masters Abstracts International, Volume: 55-04.
Contained By:	Masters Abstracts International55-04(E).
標題:	Landscape architecture. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10103836
ISBN:	9781339673790

Similarities and dissimilarities of landscape artists.
Li, Na.

Similarities and dissimilarities of landscape artists. - 137 p.

Source: Masters Abstracts International, Volume: 55-04.

Thesis (M.A.)--Michigan State University, 2016.

Landscape artists express their interpretations for landscapes in different media and by various tools. The choices of media, tools, and techniques to draw or paint are the artists' principles to differentiate them. This study discovers how similar and different these artists are. This research transforms artists' principles into the numerical data, and then analyzes the data through Principal Component Analysis (PCA). The raw data are from the techniques or characters that the author studies derived from articles and books, and the observations of artists' artworks. The selected artists are Mike Lin, Jon Burley, Robert Oliver, Michael Doyle, Robert Wood, William Kent, and Claude Monet. The result of PCA indicates that six valid dimensions explain all variances, and are based on one hundred and fifty-six principles. The first two components, with 48.66% variance explained, depict that the artist can be grouped to three different clusters: graphic artists, space designers, and painters.

ISBN: 9781339673790Subjects--Topical Terms:

541842
Landscape architecture.

Similarities and dissimilarities of landscape artists.
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520 $a Landscape artists express their interpretations for landscapes in different media and by various tools. The choices of media, tools, and techniques to draw or paint are the artists' principles to differentiate them. This study discovers how similar and different these artists are. This research transforms artists' principles into the numerical data, and then analyzes the data through Principal Component Analysis (PCA). The raw data are from the techniques or characters that the author studies derived from articles and books, and the observations of artists' artworks. The selected artists are Mike Lin, Jon Burley, Robert Oliver, Michael Doyle, Robert Wood, William Kent, and Claude Monet. The result of PCA indicates that six valid dimensions explain all variances, and are based on one hundred and fifty-six principles. The first two components, with 48.66% variance explained, depict that the artist can be grouped to three different clusters: graphic artists, space designers, and painters.
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