Communications of the ACM - Special issue on parallelism
Robust regression and outlier detection
Robust regression and outlier detection
Temporal sequence learning and data reduction for anomaly detection
ACM Transactions on Information and System Security (TISSEC)
ACM Computing Surveys (CSUR)
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
Data mining: concepts and techniques
Data mining: concepts and techniques
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Outlier Detection Using Replicator Neural Networks
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Rough Set Approach to the Survival Analysis
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
A Comparative Study of RNN for Outlier Detection in Data Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Interestingness, Peculiarity, and Multi-database Mining
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining distance-based outliers in near linear time with randomization and a simple pruning rule
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
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
An initialization method for the K-Means algorithm using neighborhood model
Computers & Mathematics with Applications
An information entropy-based approach to outlier detection in rough sets
Expert Systems with Applications: An International Journal
Neighborhood outlier detection
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A framework for clustering categorical time-evolving data
IEEE Transactions on Fuzzy Systems
A hybrid approach to outlier detection based on boundary region
Pattern Recognition Letters
Algorithms for detecting outliers via clustering and ranks
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Artificial Intelligence Review
Hi-index | 12.06 |
''One person's noise is another person's signal'' (Knorr, E., Ng, R. (1998). Algorithms for mining distance-based outliers in large datasets. In Proceedings of the 24th VLDB conference, New York (pp. 392-403)). In recent years, much attention has been given to the problem of outlier detection, whose aim is to detect outliers - objects which behave in an unexpected way or have abnormal properties. Detecting such outliers is important for many applications such as criminal activities in electronic commerce, computer intrusion attacks, terrorist threats, agricultural pest infestations, etc. And outlier detection is critically important in the information-based society. In this paper, we discuss some issues about outlier detection in rough set theory which emerged about 20 years ago, and is nowadays a rapidly developing branch of artificial intelligence and soft computing. First, we propose a novel definition of outliers in information systems of rough set theory -sequence-based outliers. An algorithm to find such outliers in rough set theory is also given. The effectiveness of sequence-based method for outlier detection is demonstrated on two publicly available databases. Second, we introduce traditional distance-based outlier detection to rough set theory and discuss the definitions of distance metrics for distance-based outlier detection in rough set theory.