南開科技大學圖書館 |

Language: 繁體中文

說明(常見問題)

南開科技大學

圖書館首頁

編目中圖書申請

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Information visualization design for...

Seo, Jinwook.

Information visualization design for multidimensional data: Integrating the rank-by-feature framework with hierarchical clustering.

紀錄類型:	書目-電子資源 : 單行本
正題名/作者:	Information visualization design for multidimensional data: Integrating the rank-by-feature framework with hierarchical clustering./
作者:	Seo, Jinwook.
面頁冊數:	227 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 3241.
Contained By:	Dissertation Abstracts International66-06B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3178635
ISBN:	9780542183775

Information visualization design for multidimensional data: Integrating the rank-by-feature framework with hierarchical clustering.
Seo, Jinwook.

Information visualization design for multidimensional data: Integrating the rank-by-feature framework with hierarchical clustering. - 227 p.

Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 3241.

Thesis (Ph.D.)--University of Maryland, College Park, 2005.

Interactive exploration of multidimensional data sets is challenging because: (1) it is difficult to comprehend patterns in more than three dimensions, and (2) current systems are often a patchwork of graphical and statistical methods leaving many researchers uncertain about how to explore their data in an orderly manner.

ISBN: 9780542183775Subjects--Topical Terms:

1000005419
Computer Science.

Information visualization design for multidimensional data: Integrating the rank-by-feature framework with hierarchical clustering.
LDR:03514nmm 2200277 4500 001 1000004715
005 20061114130245.5
008 061114s2005 eng d
020 $a 9780542183775
035 $a (UnM)AAI3178635
035 $a AAI3178635
040 $a UnM $c UnM{me_controlnum}
100 1 $a Seo, Jinwook. $3 1000005780
245 1 0 $a Information visualization design for multidimensional data: Integrating the rank-by-feature framework with hierarchical clustering.
300 $a 227 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 3241.
500 $a Chair: Ben Shneiderman.
502 $a Thesis (Ph.D.)--University of Maryland, College Park, 2005.
520 $a Interactive exploration of multidimensional data sets is challenging because: (1) it is difficult to comprehend patterns in more than three dimensions, and (2) current systems are often a patchwork of graphical and statistical methods leaving many researchers uncertain about how to explore their data in an orderly manner.
520 $a This dissertation offers a set of principles and a novel rank-by-feature framework that could enable users to better understand multidimensional and multivariate data by systematically studying distributions in one (1D) or two dimensions (2D), and then discovering relationships, clusters, gaps, outliers, and other features. Users of this rank-by-feature framework can view graphical presentations (histograms, boxplots, and scatterplots), and then choose a feature detection criterion to rank ID or 2D axis-parallel projections. By combining information visualization techniques (overview, coordination, and dynamic query) with summaries and statistical methods, users can systematically examine the most important 1D and 2D axis-parallel projections. This research provides a number of valuable contributions: (a) Graphics, Ranking, and Interaction for Discovery (GRID) principles---a set of principles for exploratory analysis of multidimensional data, which are summarized as: (1) study 1D, study 2D, then find features (2) ranking guides insight, statistics confirm. GRID principles help users organize their discovery process in an orderly manner so as to produce more thorough analyses and extract deeper insights in any multidimensional data application. (b) Rank-by-feature framework---a user interface framework based on the GRID principles. Interactive information visualization techniques are combined with statistical methods and data mining algorithms to enable users to orderly examine multidimensional data sets using ID and 2D projections. (c) The design and implementation of the Hierarchical Clustering Explorer (HCE), an information visualization tool available at www.cs.umd.edu/hcil/hce. HCE implements the rank-by-feature framework and supports interactive exploration of hierarchical clustering results to reveal one of the important features---clusters. (d) Validation through case studies and user surveys: Case studies with motivated experts in three research fields and a user survey via emails to a wide range of HCE users demonstrated the efficacy of HCE and the rank-by-feature framework. These studies also revealed potential improvement opportunities in terms of design and implementation.
590 $a School code: 0117.
650 4 $a Computer Science. $3 1000005419
690 $a 0984
710 2 0 $a University of Maryland, College Park. $3 1000005512
773 0 $t Dissertation Abstracts International $g 66-06B.
790 1 0 $a Shneiderman, Ben, $e advisor
790 $a 0117
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3178635 $z