LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Discovering cluster-based local outliers
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
A comprehensive survey of numeric and symbolic outlier mining techniques
Intelligent Data Analysis
An approach based on wavelet analysis and non-linear mapping to detect anomalies in dataset
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
A fast greedy algorithm for outlier mining
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
An optimization model for outlier detection in categorical data
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Collusion set detection through outlier discovery
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
Detection of Outlier Residues for Improving Interface Prediction in Protein Heterocomplexes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Existing proposals on outlier detection didn't take the semantic knowledge of the dataset into consideration. They only tried to find outliers from dataset itself, which prevents finding more meaningful outliers. In this paper, we consider the problem of outlier detection integrating semantic knowledge. We introduce new definition for outlier: semantic outlier. A semantic outlier is a data point, which behaves differently with other data points in the same class. A measure for identifying the degree of each object being an outlier is presented, which is called semantic outlier factor (SOF). An efficient algorithm for mining semantic outliers based on SOF is also proposed. Experimental results show that meaningful and interesting outliers can be found with our method.