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 neighbours to determine the degree to which the object deviates from its neighbourhood. We present theoretical bounds on LDOF’s false-detection probability. Experimentally, LDOF compares favorably to classical KNN and LOF based outlier detection. In particular it is less sensitive to parameter values.
Keywordslocal outlier scattered data k-distance KNN LOF LDOF
Unable to display preview. Download preview PDF.
- Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J.: LOF: Identifying density-based local outliers. In: SIGMOD Conference, pp. 93–104 (2000)Google Scholar
- Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density- based algorithm for discovering clusters in large spatial databases with noise. In: KDD, pp. 226–231 (1996)Google Scholar
- Knorr, E.M., Ng, R.T.: Algorithms for mining distance-based outliers in large datasets. In: VLDB, pp. 392–403 (1998)Google Scholar
- Kriegel, H.-P., Schubert, M., Zimek, A.: Angle-based outlier detection in high-dimensional data. In: KDD, pp. 444–452 (2008)Google Scholar
- Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algo- rithms for mining outliers from large data sets. In: SIGMOD Conference, pp. 427–438 (2000)Google Scholar