Robust regression and outlier detection
Robust regression and outlier detection
C4.5: programs for machine learning
C4.5: programs for machine learning
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
On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Structures and Algorithms
Data Structures and Algorithms
Machine Learning
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Enhancing Effectiveness of Outlier Detections for Low Density Patterns
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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
Support Vector Data Description
Machine Learning
LOADED: Link-Based Outlier and Anomaly Detection in Evolving Data Sets
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
The Art of Computer Programming, Volume 4, Fascicle 3: Generating All Combinations and Partitions
The Art of Computer Programming, Volume 4, Fascicle 3: Generating All Combinations and Partitions
Feature bagging for outlier detection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Mining distance-based outliers from large databases in any metric space
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Outlier detection by active learning
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Outlier detection by sampling with accuracy guarantees
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
ROCR: visualizing classifier performance in R
Bioinformatics
IEEE Transactions on Knowledge and Data Engineering
DOLPHIN: An efficient algorithm for mining distance-based outliers in very large datasets
ACM Transactions on Knowledge Discovery from Data (TKDD)
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
ACM Computing Surveys (CSUR)
Spectrum of variable-random trees
Journal of Artificial Intelligence Research
Filtering and Refinement: A Two-Stage Approach for Efficient and Effective Anomaly Detection
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
On detecting clustered anomalies using SCiForest
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
A nonparametric outlier detection for effectively discovering top-n outliers from engineering data
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A unified subspace outlier ensemble framework for outlier detection
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
Proceedings of the 21st ACM international conference on Information and knowledge management
Exploiting domain knowledge to detect outliers
Data Mining and Knowledge Discovery
Ensembles for unsupervised outlier detection: challenges and research questions a position paper
ACM SIGKDD Explorations Newsletter
| Hi-index | 0.00 |
Anomalies are data points that are few and different. As a result of these properties, we show that, anomalies are susceptible to a mechanism called isolation. This article proposes a method called Isolation Forest (iForest), which detects anomalies purely based on the concept of isolation without employing any distance or density measure---fundamentally different from all existing methods. As a result, iForest is able to exploit subsampling (i) to achieve a low linear time-complexity and a small memory-requirement and (ii) to deal with the effects of swamping and masking effectively. Our empirical evaluation shows that iForest outperforms ORCA, one-class SVM, LOF and Random Forests in terms of AUC, processing time, and it is robust against masking and swamping effects. iForest also works well in high dimensional problems containing a large number of irrelevant attributes, and when anomalies are not available in training sample.