LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining top-n local outliers in large databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Semi-Supervised Adapted HMMs for Unusual Event Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Semi-supervised outlier detection
Proceedings of the 2006 ACM symposium on Applied computing
A Neural Network-Based Novelty Detector for Image Sequence Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Anomaly-Based Intrusion Detection using Fuzzy Rough Clustering
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01
A Novel Data Mining Method for Network Anomaly Detection Based on Transductive Scheme
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Outliers in rough k-means clustering
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
An improved clustering algorithm for information granulation
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Finding key attribute subset in dataset for outlier detection
Knowledge-Based Systems
Fast pedestrian detection system with a two layer cascade of classifiers
Computers & Mathematics with Applications
A multivariate fuzzy system applied for outliers detection
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper presents a fuzzy rough semi-supervised outlier detection (FRSSOD) approach with the help of some labeled samples and fuzzy rough C-means clustering. This method introduces an objective function, which minimizes the sum squared error of clustering results and the deviation from known labeled examples as well as the number of outliers. Each cluster is represented by a center, a crisp lower approximation and a fuzzy boundary by using fuzzy rough C-means clustering and only those points located in boundary can be further discussed the possibility to be reassigned as outliers. As a result, this method can obtain better clustering results for normal points and better accuracy for outlier detection. Experiment results show that the proposed method, on average, keep, or improve the detection precision and reduce false alarm rate as well as reduce the number of candidate outliers to be discussed.