DocumentCode
2545189
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
fYear
2012
fDate
29-31 May 2012
Firstpage
1632
Lastpage
1636
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on
Conference_Location
Sichuan
Print_ISBN
978-1-4673-0025-4
Type
conf
DOI
10.1109/FSKD.2012.6233948
Filename
6233948
Link To Document