DocumentCode :
3648524
Title :
Improved Bisector pruning for uncertain data mining
Author :
Ivica Lukić;Mirko Kohler;Ninoslav Slavek
Author_Institution :
Faculty of Electrical Engineering, J. J. Strossmayer University of Osijek, Croatia
fYear :
2012
fDate :
6/1/2012 12:00:00 AM
Firstpage :
355
Lastpage :
360
Abstract :
Uncertain data mining is well studied and very challenging task. This paper is concentrated on clustering uncertain objects with location uncertainty. Uncertain locations are described by probability density function (PDF). Number of uncertain objects can be very large and obtaining quality result within reasonable time is a challenging task. Basic clustering method is UK-means, in which all expected distances (ED) from objects to clusters are calculated. Thus UK-means is inefficient. To avoid ED calculations various pruning methods are proposed. The pruning methods are significantly more effective than UK-means method. In this paper, Improved Bisector pruning method is proposed as an improvement of clustering process.
Keywords :
"Uncertainty","Probability density function","Data mining","Clustering methods","Clustering algorithms","Computational efficiency","Measurement errors"
Publisher :
ieee
Conference_Titel :
Information Technology Interfaces (ITI), Proceedings of the ITI 2012 34th International Conference on
ISSN :
1334-2762
Print_ISBN :
978-1-4673-1629-3
Type :
conf
DOI :
10.2498/iti.2012.0353
Filename :
6308032
Link To Document :
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