3D Visual Data Mining: goals and experiences

Michael Hanspeter Bøhlen; Linas Bukauskas; Poul Svante Eriksen; Steffen Lilholt Lauritzen; Arturas Mazeika; Peter Musaeus; Peer Mylov

3D Visual Data Mining: goals and experiences

Bidragets oversatte titel: 3D visual data mining: goals and experiences

Michael Hanspeter Bøhlen, Linas Bukauskas, Poul Svante Eriksen, Steffen Lilholt Lauritzen, Arturas Mazeika, Peter Musaeus, Peer Mylov

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

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Abstract

The visual exploration of large databases raises a number of unresolved inference problems and calls for new interaction patterns between multiple disciplines--both at the conceptual and technical level. We present an approach that is based on the interaction of four disciplines: database systems, statistical analyses, perceptual and cognitive psychology, and scientific visualization. At the conceptual level we offer perceptual and cognitive insights to guide the information visualization process. We then choose cluster surfaces to exemplify the data mining process, to discuss the tasks involved, and to work out the interaction patterns.
Udgivelsesdato: AUG 28

Bidragets oversatte titel	3D visual data mining: goals and experiences
Originalsprog	Engelsk
Tidsskrift	Computational Statistics & Data Analysis
Vol/bind	43
Udgave nummer	4
Sider (fra-til)	445 - 469
ISSN	0167-9473
Status	Udgivet - 2003

Emneord

Immersive data exploration
Nested density surfaces
Observer relative data extraction
Perception of space and objects
Visual data mining

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TY - JOUR

T1 - 3D Visual Data Mining: goals and experiences

AU - Bøhlen, Michael Hanspeter

AU - Bukauskas, Linas

AU - Eriksen, Poul Svante

AU - Lauritzen, Steffen Lilholt

AU - Mazeika, Arturas

AU - Musaeus, Peter

AU - Mylov, Peer

PY - 2003

Y1 - 2003

N2 - The visual exploration of large databases raises a number of unresolved inference problems and calls for new interaction patterns between multiple disciplines--both at the conceptual and technical level. We present an approach that is based on the interaction of four disciplines: database systems, statistical analyses, perceptual and cognitive psychology, and scientific visualization. At the conceptual level we offer perceptual and cognitive insights to guide the information visualization process. We then choose cluster surfaces to exemplify the data mining process, to discuss the tasks involved, and to work out the interaction patterns.

AB - The visual exploration of large databases raises a number of unresolved inference problems and calls for new interaction patterns between multiple disciplines--both at the conceptual and technical level. We present an approach that is based on the interaction of four disciplines: database systems, statistical analyses, perceptual and cognitive psychology, and scientific visualization. At the conceptual level we offer perceptual and cognitive insights to guide the information visualization process. We then choose cluster surfaces to exemplify the data mining process, to discuss the tasks involved, and to work out the interaction patterns.

KW - Immersive data exploration

KW - Nested density surfaces

KW - Observer relative data extraction

KW - Perception of space and objects

KW - Visual data mining

KW - Immersive data exploration

KW - Nested density surfaces

KW - Observer relative data extraction

KW - Perception of space and objects

KW - Visual data mining

M3 - Journal article

SN - 0167-9473

VL - 43

SP - 445

EP - 469

JO - Computational Statistics & Data Analysis

JF - Computational Statistics & Data Analysis

IS - 4

ER -

3D Visual Data Mining: goals and experiences

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