Finding centric local outliers in categorical/numerical spaces

Outlier detection techniques are widely used in many applications such as credit card fraud detection, monitoring criminal activities in electronic commerce, etc. These applications attempt to identify outliers as noises, exceptions, or objects around the border. The existing density-based local outlier detection assigns the degree to an object of being an outlier in a numerical space. In this paper, we propose a novel mutualreinforcement based local outlier detection approach. Instead of detecting local outliers as noise, we attempt to identify local outliers in center, which are similar with some clusters of objects on the one hand, and are unique on the other hand. Our technique can be used for bank investment to identify a unique body, similar with many good competitors, to invest. We attempt to detect local outliers in categorical, ordinal as well as numerical data. In categorical data, the challenging is that there are many similar but different ways to specify relationships amon...

• Type: Journal
• Year: 2006
• Where: KAIS
• Authors: Jeffrey Xu Yu, Weining Qian, Hongjun Lu, Aoying Zhou
• Keywords: KAIS 2006, Local Outliers, Outlier Detection, Outliers