3D visual data mining: goals and experiences

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Standard

3D visual data mining : goals and experiences. / Bohlen, M; Bukauskas, L; Eriksen, PS; Lauritzen, SL; Mazeika, A; Musaeus, P; Mylov, P.

I: Computational Statistics & Data Analysis, Bind 43, Nr. 4, 2003, s. 445-469.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Bohlen, M, Bukauskas, L, Eriksen, PS, Lauritzen, SL, Mazeika, A, Musaeus, P & Mylov, P 2003, '3D visual data mining: goals and experiences', Computational Statistics & Data Analysis, bind 43, nr. 4, s. 445-469. https://doi.org/10.1016/S0167-9473(02)00287-6

APA

Bohlen, M., Bukauskas, L., Eriksen, PS., Lauritzen, SL., Mazeika, A., Musaeus, P., & Mylov, P. (2003). 3D visual data mining: goals and experiences. Computational Statistics & Data Analysis, 43(4), 445-469. https://doi.org/10.1016/S0167-9473(02)00287-6

Vancouver

Bohlen M, Bukauskas L, Eriksen PS, Lauritzen SL, Mazeika A, Musaeus P o.a. 3D visual data mining: goals and experiences. Computational Statistics & Data Analysis. 2003;43(4):445-469. https://doi.org/10.1016/S0167-9473(02)00287-6

Author

Bohlen, M ; Bukauskas, L ; Eriksen, PS ; Lauritzen, SL ; Mazeika, A ; Musaeus, P ; Mylov, P. / 3D visual data mining : goals and experiences. I: Computational Statistics & Data Analysis. 2003 ; Bind 43, Nr. 4. s. 445-469.

Bibtex

@article{589a511723cc4028980437e3a0f92aef,
title = "3D visual data mining: goals and experiences",
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.",
author = "M Bohlen and L Bukauskas and PS Eriksen and SL Lauritzen and A Mazeika and P Musaeus and P Mylov",
year = "2003",
doi = "10.1016/S0167-9473(02)00287-6",
language = "English",
volume = "43",
pages = "445--469",
journal = "Computational Statistics and Data Analysis",
issn = "0167-9473",
publisher = "Elsevier",
number = "4",

}

RIS

TY - JOUR

T1 - 3D visual data mining

T2 - goals and experiences

AU - Bohlen, M

AU - Bukauskas, L

AU - Eriksen, PS

AU - Lauritzen, SL

AU - Mazeika, A

AU - Musaeus, P

AU - Mylov, P

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.

U2 - 10.1016/S0167-9473(02)00287-6

DO - 10.1016/S0167-9473(02)00287-6

M3 - Journal article

VL - 43

SP - 445

EP - 469

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

SN - 0167-9473

IS - 4

ER -

ID: 127504916