Improved Bisector Pruning for Uncertain Data Mining (CROSBI ID 588065)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Lukić Ivica ; Köhler Mirko ; Slavek Ninoslav
engleski
Improved Bisector Pruning for Uncertain Data Mining
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.
Clustering; data mining; expected distance; pruning; uncertain data
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Podaci o prilogu
355-360.
2012.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of the 34th International Conference on Information Technology Interfaces (ITI 2012)
Luzar-Stiffler, Vesna ; Jarec, Iva ; Bekic, Zoran
Zagreb: Sveučilišni računski centar Sveučilišta u Zagrebu (Srce)
978-953-7138-24-0
1330-1012
Podaci o skupu
34th International Conference on Information Technology Interfaces ITI 2012
predavanje
25.06.2012-28.06.2012
Cavtat, Hrvatska