A New Local Distance-based Outlier Detection Approach for Scattered Real-World Data
Detecting outliers which are grossly different from or inconsistent with the remaining dataset is a major challenge in real-world KDD applications. Existing outlier detection methods are ineffective on scattered real-world datasets due to implicit data patterns and parameter setting issues. We define a novel Local Distance-based Outlier Factor (LDOF) to measure the outlier-ness of objects in scattered datasets which addresses these issues. LDOF uses the relative location of an object to its...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the 13th Asia-Pacific Conference on Knowledge Discovery and Data Mining (PAKDD'09)|
|01_Zhang_A_New_Local_Distance-based_2009.pdf||541.61 kB||Adobe PDF||Request a copy|
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