Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
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
Mining top-n local outliers in large databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Linearly Combining Density Estimators via Stacking
Machine Learning
HD-Eye: Visual Mining of High-Dimensional Data
IEEE Computer Graphics and Applications
Bayesian Averaging of Classifiers and the Overfitting Problem
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Evaluating Boosting Algorithms to Classify Rare Classes: Comparison and Improvements
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Bootstrapping a data mining intrusion detection system
Proceedings of the 2003 ACM symposium on Applied computing
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
Comparing Bayes model averaging and stacking when model approximation error cannot be ignored
The Journal of Machine Learning Research
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Feature bagging for outlier detection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Converting Output Scores from Outlier Detection Algorithms into Probability Estimates
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
ACM Computing Surveys (CSUR)
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Statistical selection of relevant subspace projections for outlier ranking
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
A unified subspace outlier ensemble framework for outlier detection
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
Mining outliers with ensemble of heterogeneous detectors on random subspaces
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
HiCS: High Contrast Subspaces for Density-Based Outlier Ranking
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Outlier Analysis
Outlier Ranking via Subspace Analysis in Multiple Views of the Data
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
Flexible and adaptive subspace search for outlier analysis
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Ensembles for unsupervised outlier detection: challenges and research questions a position paper
ACM SIGKDD Explorations Newsletter
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Ensemble analysis is a widely used meta-algorithm for many data mining problems such as classification and clustering. Numerous ensemble-based algorithms have been proposed in the literature for these problems. Compared to the clustering and classification problems, ensemble analysis has been studied in a limited way in the outlier detection literature. In some cases, ensemble analysis techniques have been implicitly used by many outlier analysis algorithms, but the approach is often buried deep into the algorithm and not formally recognized as a general-purpose meta-algorithm. This is in spite of the fact that this problem is rather important in the context of outlier analysis. This paper discusses the various methods which are used in the literature for outlier ensembles and the general principles by which such analysis can be made more effective. A discussion is also provided on how outlier ensembles relate to the ensemble-techniques used commonly for other data mining problems.