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High Dimensional Visual Data Classification.

Authors :
Hutchison, David
Kanade, Takeo
Kittler, Josef
Kleinberg, Jon M.
Mattern, Friedemann
Mitchell, John C.
Naor, Moni
Nierstrasz, Oscar
Rangan, C. Pandu
Steffen, Bernhard
Sudan, Madhu
Terzopoulos, Demetri
Tygar, Doug
Vardi, Moshe Y.
Weikum, Gerhard
Lévy, Pierre P
Le Grand, Bénédicte
Soto, Michel
Darago, Laszlo
Toubiana, Laurent
Source :
Pixelization Paradigm; 2007, p25-34, 10p
Publication Year :
2007

Abstract

We present new visual data mining algorithms for interactive decision tree construction with large datasets. The size of data stored in the world is constantly increasing but the limits of current visual data mining (and visualization) methods concerning the number of items and dimensions of the dataset treated are well known (even with pixellisation methods). One solution to improve these methods is to use a higher-level representation of the data, for example a symbolic data representation. Our new interactive decision tree construction algorithms deal with interval and taxonomical data. With such a representation, we are able to deal with potentially very large datasets because we do not use the original data but higher-level data representation. Interactive algorithms are examples of new data mining approach aiming at involving more intensively the user in the process. The main advantages of this user-centered approach are the increased confidence and comprehensibility of the obtained model, because the user was involved in its construction and the possible use of human pattern recognition capabilities. We present some results we obtained on very large datasets. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783540710264
Database :
Complementary Index
Journal :
Pixelization Paradigm
Publication Type :
Book
Accession number :
33094770
Full Text :
https://doi.org/10.1007/978-3-540-71027-1_3