A probabilistic resource allocating network for novelty detection
Neural Computation
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
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
Intrusion detection in wireless ad-hoc networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scaling mining algorithms to large databases
Communications of the ACM - Evolving data mining into solutions for insights
Robust Parameterized Component Analysis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Anomaly Detection over Noisy Data using Learned Probability Distributions
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Rule-based anomaly pattern detection for detecting disease outbreaks
Eighteenth national conference on Artificial intelligence
Novelty detection: a review—part 1: statistical approaches
Signal Processing
On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms
Data Mining and Knowledge Discovery
Rapid detection of significant spatial clusters
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
Anomaly detection and spatio-temporal analysis of global climate system
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
On community outliers and their efficient detection in information networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Spatio-temporal outlier detection based on context: a summary of results
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Mining at most top-K% spatio-temporal outlier based context: a summary of results
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Isolation-Based Anomaly Detection
ACM Transactions on Knowledge Discovery from Data (TKDD)
Mining multidimensional contextual outliers from categorical relational data
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Review: A review of novelty detection
Signal Processing
Hi-index | 0.00 |
When anomaly detection software is used as a data analysis tool, finding the hardest-to-detect anomalies is not the most critical task. Rather, it is often more important to make sure that those anomalies that are reported to the user are in fact interesting. If too many unremarkable data points are returned to the user labeled as candidate anomalies, the software will soon fall into disuse. One way to ensure that returned anomalies are useful is to make use of domain knowledge provided by the user. Often, the data in question includes a set of environmental attributes whose values a user would never consider to be directly indicative of an anomaly. However, such attributes cannot be ignored because they have a direct effect on the expected distribution of the result attributes whose values can indicate an anomalous observation. This paper describes a general purpose method called conditional anomaly detection for taking such differences among attributes into account, and proposes three different expectation-maximization algorithms for learning the model that is used in conditional anomaly detection. Experiments with more than 13 different data sets compare our algorithms with several other more standard methods for outlier or anomaly detection.