Title :
Detecting global outliers from large distributed databases
Author :
Ji Zhang ; Jie Cao ; Xiaodong Zhu
Author_Institution :
Dept. of Math. & Comput., Univ. of Southern Queensland, Toowoomba, QLD, Australia
Abstract :
In this paper, we present an innovative system, coined as DISTROD (a.k.a DISTRibuted Outlier Detector), for detecting outliers from distributed databases. DISTROD is able to effectively detect the so-called global outliers from distributed databases that are consistent with those produced by the centralized detection paradigm. Experimental evaluation demonstrates the good performance of DISTROD in terms of effectiveness and speed.
Keywords :
data mining; distributed databases; security of data; DISTROD; centralized detection paradigm; data mining; distributed databases; distributed outlier detector; global outlier detection; innovative system; Computer architecture; Data mining; Distributed databases; Kernel; Microprocessors; Spatial databases;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on
Conference_Location :
Sichuan
Print_ISBN :
978-1-4673-0025-4
DOI :
10.1109/FSKD.2012.6233948
