Outlier Detection in Arbitrarily Oriented Subspaces
作者： Hans-Peter Kriegel , Peer Kroger , Erich Schubert , Arthur Zimek
DOI: 10.1109/ICDM.2012.21
关键词: Feature vector 、 Unsupervised learning 、 Local outlier factor 、 Pattern recognition 、 Linear subspace 、 Mathematics 、 Data mining 、 Complement (set theory) 、 Outlier 、 Data set 、 Anomaly detection 、 Artificial intelligence
摘要: In this paper, we propose a novel outlier detection model to find outliers that deviate from the generating mechanisms of normal instances by considering combinations different subsets attributes, as they occur when there are local correlations in data set. Our enables search for arbitrarily oriented subspaces original feature space. We show how addition an score, our also derives explanation outlierness is useful investigating results. experiments suggest method can than existing work and be seen complement those approaches.
computer.org 
acm.org 
sci-hub.se 参考文章(43)
Arthur Zimek, Hans-Peter Kriegel, Erich Schubert, Peer Kröger, Interpreting and Unifying Outlier Scores siam international conference on data mining. pp. 13- 24 ,(2011)
Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek, Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data Advances in Knowledge Discovery and Data Mining. pp. 831- 838 ,(2009) , 10.1007/978-3-642-01307-2_86
Fabrizio Angiulli, Clara Pizzuti, Fast Outlier Detection in High Dimensional Spaces european conference on principles of data mining and knowledge discovery. pp. 15- 26 ,(2002) , 10.1007/3-540-45681-3_2
Raymond T. Ng, Edwin M. Knorr, Algorithms for Mining Distance-Based Outliers in Large Datasets very large data bases. pp. 392- 403 ,(1998)
Ji Zhang, Meng Lou, Tok Wang Ling, Hai Wang, Hos-Miner: a system for detecting outlyting subspaces of high-dimensional data very large data bases. pp. 1265- 1268 ,(2004) , 10.1016/B978-012088469-8.50123-6
Jian Tang, Zhixiang Chen, Ada Wai-chee Fu, David W. Cheung, Enhancing Effectiveness of Outlier Detections for Low Density Patterns knowledge discovery and data mining. pp. 535- 548 ,(2002) , 10.1007/3-540-47887-6_53
Hoang Vu Nguyen, Vivekanand Gopalkrishnan, Ira Assent, An Unbiased Distance-Based Outlier Detection Approach for High-Dimensional Data Database Systems for Advanced Applications. pp. 138- 152 ,(2011) , 10.1007/978-3-642-20149-3_12
Emmanuel Müller, Matthias Schiffer, Patrick Gerwert, Matthias Hannen, Timm Jansen, Thomas Seidl, SOREX: subspace outlier ranking exploration toolkit european conference on machine learning. pp. 607- 610 ,(2010) , 10.1007/978-3-642-15939-8_44
Hans-Peter Kriegel, Matthias S hubert, Arthur Zimek, Angle-based outlier detection in high-dimensional data Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08. pp. 444- 452 ,(2008) , 10.1145/1401890.1401946
Fabian Keller, Emmanuel Muller, Klemens Bohm, HiCS: High Contrast Subspaces for Density-Based Outlier Ranking 2012 IEEE 28th International Conference on Data Engineering. pp. 1037- 1048 ,(2012) , 10.1109/ICDE.2012.88