ODDC: outlier detection using distance distribution clustering

Authors:
Kun Niu;Chong Huang;Shubo Zhang;Junliang Chen
Affiliations:
Dept. of Computer Sci. and Eng., Beijing University of Posts and Telecommunications, Beijing, China;Graduate University, Chinese Academy of Sciences, Beijing, China;Dept. of Strategy Research, China Telecom Beijing Institute, Beijing, China;Dept. of Computer Sci. and Eng., Beijing University of Posts and Telecommunications, Beijing, China
Venue:
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Year:
2007

Citing 9
Cited 4

LOF: identifying density-based local outliers

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Robust Classification for Imprecise Environments

Machine Learning
Outlier Detection Using k-Nearest Neighbour Graph

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
A vertical distance-based outlier detection method with local pruning

Proceedings of the thirteenth ACM international conference on Information and knowledge management
Outlier Mining in Large High-Dimensional Data Sets

IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
Feature bagging for outlier detection

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Distance-Based Detection and Prediction of Outliers

IEEE Transactions on Knowledge and Data Engineering
Outlier detection by active learning

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

Fuzzy clustering-based approach for outlier detection

ACE'10 Proceedings of the 9th WSEAS international conference on Applications of computer engineering
New outlier detection method based on fuzzy clustering

WSEAS Transactions on Information Science and Applications
A hybrid fraud scoring and spike detection technique in streaming data

Intelligent Data Analysis
A minimum spanning tree-inspired clustering-based outlier detection technique

ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects

Quantified Score

Hi-index	0.00

Visualization

Abstract

Outlier detection is an important issue in many industrial and financial applications. Most outlier detection methods suffer from two problems: First, they need parameter tuning in accord to domain knowledge. Second, they are incapable to scale up to high dimensional space. In this paper, we propose a distance-based outlier definition and a detection algorithm ODDC (Distribution Clustering Outlier Detection). We redefine the problem by clustering in the distribution difference space rather than the original feature space. As a result, the new algorithm is stable regardless of different input and scalable to the dimensionality. Experiments on both synthetic and real datasets show that ODDC outperforms the counterpart both in effectiveness and efficiency.