BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 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
Mining top-n local outliers in large databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
STR: A Simple and Efficient Algorithm for R-Tree Packing
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
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
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
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
Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
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
Angle-based outlier detection in high-dimensional data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Harnessing the strengths of anytime algorithms for constant data streams
Data Mining and Knowledge Discovery
Anytime classification for a pool of instances
Machine Learning
LoOP: local outlier probabilities
Proceedings of the 18th ACM conference on Information and knowledge management
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
Bulk loading hierarchical mixture models for efficient stream classification
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Novel data stream pattern mining report on the StreamKDD'10 workshop
ACM SIGKDD Explorations Newsletter
AnyOut: anytime outlier detection on streaming data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
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Data streams are gaining importance in many sensoring and monitoring environments. Frequent mining tasks on data streams include classification, modeling and outlier detection. Since often the data arrival rates vary, anytime algorithms have been proposed for stream clustering and classification, which can deliver a fast first result and improve their result if more time is available. In this work, we propose the novel concept of anytime outlier detection and introduce an algorithm for anytime outlier detection based on a hierarchical cluster representation. We show promising results in preliminary experiments and discuss future research for anytime outlier detection.