Communications of the ACM
Temporal sequence learning and data reduction for anomaly detection
ACM Transactions on Information and System Security (TISSEC)
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
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Fast Outlier Detection in High Dimensional Spaces
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Finding Intensional Knowledge of Distance-Based Outliers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Outlier Detection: An Approximate Reasoning Approach
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Outlier Detection Based on Granular Computing
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Outlier detection based on rough sets theory
Intelligent Data Analysis
A Predictive Analysis on Medical Data Based on Outlier Detection Method Using Non-Reduct Computation
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
An information entropy-based approach to outlier detection in rough sets
Expert Systems with Applications: An International Journal
A hybrid approach to outlier detection based on boundary region
Pattern Recognition Letters
Outlier detection based on rough membership function
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Review: A review of novelty detection
Signal Processing
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In this paper, we suggest to exploit the framework of rough set for detecting outliers — individuals who behave in an unexpected way or feature abnormal properties. The ability to locate outliers can help to maintain knowledge base integrity and to single out irregular individuals. First, we formally define the notions of exceptional set and minimal exceptional set. We then analyze some special cases of exceptional set and minimal exceptional set. Finally, we introduce a new definition for outliers as well as the definition of exceptional degree. Through calculating the exceptional degree for each object in minimal exceptional sets, we can find out all outliers in a given dataset.