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
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Two-phase clustering process for outliers detection
Pattern Recognition Letters
Findout: finding outliers in very large datasets
Knowledge and Information Systems
Outlier Detection Integrating Semantic Knowledge
WAIM '02 Proceedings of the Third International Conference on Advances in Web-Age Information Management
Outlier Detection Using Replicator Neural Networks
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Discovering cluster-based local outliers
Pattern Recognition Letters
A Comparative Study of RNN for Outlier Detection in Data Mining
ICDM '02 Proceedings of the 2002 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
Feature bagging for outlier detection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Estimating the Support of a High-Dimensional Distribution
Neural Computation
A unified subspace outlier ensemble framework for outlier detection
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
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
ACM Computing Surveys (CSUR)
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 Outlier Detection Algorithm Based on Arbitrary Shape Clustering
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes
Data Mining and Knowledge Discovery
Review: A review of novelty detection
Signal Processing
A ranking-based algorithm for detection of outliers in categorical data
International Journal of Hybrid Intelligent Systems
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The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. Recently, the problem of outlier detection in categorical data is defined as an optimization problem and a local-search heuristic based algorithm (LSA) is presented. However, as is the case with most iterative type algorithms, the LSA algorithm is still very time-consuming on very large datasets. In this paper, we present a very fast greedy algorithm for mining outliers under the same optimization model. Experimental results on real datasets and large synthetic datasets show that: (1) Our new algorithm has comparable performance with respect to those state-of-the-art outlier detection algorithms on identifying true outliers and (2) Our algorithm can be an order of magnitude faster than LSA algorithm.