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
Motion Estimation Via Cluster Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Anomaly Detection over Noisy Data using Learned Probability Distributions
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Voting-Merging: An Ensemble Method for Clustering
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Learning to match and cluster large high-dimensional data sets for data integration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
LOADED: Link-Based Outlier and Anomaly Detection in Evolving Data Sets
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Combining Multiple Clusterings by Soft Correspondence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Bioinformatics
ACM Computing Surveys (CSUR)
Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Java-ML: A Machine Learning Library
The Journal of Machine Learning Research
LoOP: local outlier probabilities
Proceedings of the 18th ACM conference on Information and knowledge management
iTopicModel: Information Network-Integrated Topic Modeling
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
On community outliers and their efficient detection in information networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Top-Eye: top-k evolving trajectory outlier detection
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Discovering clusters in motion time-series data
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Outlier detection in graph streams
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Community trend outlier detection using soft temporal pattern mining
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
On detecting association-based clique outliers in heterogeneous information networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Mining groups of common interest: discovering topical communities with network flows
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Large-scale spectral clustering on graphs
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Temporal datasets, in which data evolves continuously, exist in a wide variety of applications, and identifying anomalous or outlying objects from temporal datasets is an important and challenging task. Different from traditional outlier detection, which detects objects that have quite different behavior compared with the other objects, temporal outlier detection tries to identify objects that have different evolutionary behavior compared with other objects. Usually objects form multiple communities, and most of the objects belonging to the same community follow similar patterns of evolution. However, there are some objects which evolve in a very different way relative to other community members, and we define such objects as evolutionary community outliers. This definition represents a novel type of outliers considering both temporal dimension and community patterns. We investigate the problem of identifying evolutionary community outliers given the discovered communities from two snapshots of an evolving dataset. To tackle the challenges of community evolution and outlier detection, we propose an integrated optimization framework which conducts outlier-aware community matching across snapshots and identification of evolutionary outliers in a tightly coupled way. A coordinate descent algorithm is proposed to improve community matching and outlier detection performance iteratively. Experimental results on both synthetic and real datasets show that the proposed approach is highly effective in discovering interesting evolutionary community outliers.