Detecting global outliers from large distributed databases

Zhang, Ji and Cao, Jie and Zhu, Xiaodong (2012) Detecting global outliers from large distributed databases. In: 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012), 29-31 May 2012, Chongqing; China.

[img] PDF (Documentation)
FSKD2012.pdf

Download (398Kb)

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.


Statistics for USQ ePrint 22594
Statistics for this ePrint Item
Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published version made not accessible.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 17 May 2013 02:22
Last Modified: 29 Mar 2018 01:31
Uncontrolled Keywords: distributed database; experimental evaluation; innovative systems
Fields of Research : 08 Information and Computing Sciences > 0805 Distributed Computing > 080599 Distributed Computing not elsewhere classified
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1109/FSKD.2012.6233948
URI: http://eprints.usq.edu.au/id/eprint/22594

Actions (login required)

View Item Archive Repository Staff Only