Naslov
Improved Bisector Pruning for Uncertain Data Mining

Autori
Lukić Ivica ; Köhler Mirko ; Slavek Ninoslav

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
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), 2012, 355-360

ISBN
978-953-7138-24-0

Skup
34th International Conference on Information Technology Interfaces (ITI 2012)

Mjesto i datum
Cavtat, Hrvatska, 25.06.2012. - 28.06.2012

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Clustering; data mining; expected distance; pruning; uncertain data

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA