An approach to anytime learning
ML92 Proceedings of the ninth international workshop on Machine learning
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
ACM SIGART Bulletin
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
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
The Control of Teams of Autonomous Objects in the Time-Constrained Environments
ICAIS '02 Proceedings of the 2002 IEEE International Conference on Artificial Intelligence Systems (ICAIS'02)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms
Data Mining and Knowledge Discovery
Mining Deviants in Time Series Data Streams
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Interruptible anytime algorithms for iterative improvement of decision trees
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Example-Based Robust Outlier Detection in High Dimensional Datasets
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Online outlier detection in sensor data using non-parametric models
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Detecting distance-based outliers in streams of data
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Online Outlier Detection Based on Relative Neighbourhood Dissimilarity
WISE '08 Proceedings of the 9th international conference on Web Information Systems Engineering
Detection and Exploration of Outlier Regions in Sensor Data Streams
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Indexing density models for incremental learning and anytime classification on data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Neighbor-based pattern detection for windows over streaming data
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
SPOT: A System for Detecting Projected Outliers From High-dimensional Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Harnessing the strengths of anytime algorithms for constant data streams
Data Mining and Knowledge Discovery
Harnessing the Strengths of Anytime Algorithms for Constant Data Streams
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Unsupervised Class Separation of Multivariate Data through Cumulative Variance-Based Ranking
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Self-Adaptive Anytime Stream Clustering
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Detecting outliers on arbitrary data streams using anytime approaches
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
The Journal of Machine Learning Research
Adaptive outlierness for subspace outlier ranking
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Statistical selection of relevant subspace projections for outlier ranking
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
An effective evaluation measure for clustering on evolving data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Attribute outlier detection over data streams
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Visual evaluation of outlier detection models
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Stream data mining using the MOA framework
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Stream data mining using the MOA framework
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Big data, big business: bridging the gap
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Research issues in outlier detection for data streams
ACM SIGKDD Explorations Newsletter
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With the increase of sensor and monitoring applications, data mining on streaming data is receiving increasing research attention. As data is continuously generated, mining algorithms need to be able to analyze the data in a one-pass fashion. In many applications the rate at which the data objects arrive varies greatly. This has led to anytime mining algorithms for classification or clustering. They successfully mine data until the a priori unknown point of interruption by the next data in the stream. In this work we investigate anytime outlier detection. Anytime outlier detection denotes the problem of determining within any period of time whether an object in a data stream is anomalous. The more time is available, the more reliable the decision should be. We introduce AnyOut, an algorithm capable of solving anytime outlier detection, and investigate different approaches to build up the underlying data structure. We propose a confidence measure for AnyOut that allows to improve the performance on constant data streams. We evaluate our method in thorough experiments and demonstrate its performance in comparison with established algorithms for outlier detection.