A unifying review of linear Gaussian models
Neural Computation
A general probabilistic framework for clustering individuals and objects
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering Time Series with Clipped Data
Machine Learning
Analyzing time series gene expression data
Bioinformatics
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
An Information Theoretic Approach to Detection of Minority Subsets in Database
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Temporal causal modeling with graphical granger methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Time series clustering and classification by the autoregressive metric
Computational Statistics & Data Analysis
Multi-scale anomaly detection algorithm based on infrequent pattern of time series
Journal of Computational and Applied Mathematics
Search for Additive Nonlinear Time Series Causal Models
The Journal of Machine Learning Research
Hierarchical Clustering of Time-Series Data Streams
IEEE Transactions on Knowledge and Data Engineering
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Disk aware discord discovery: finding unusual time series in terabyte sized datasets
Knowledge and Information Systems
Clustering Distributed Time Series in Sensor Networks
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Latent feature encoding using dyadic and relational data
Proceedings of the 20th ACM international conference on Information and knowledge management
Online outlier detection for data streams
Proceedings of the 15th Symposium on International Database Engineering & Applications
Research issues in outlier detection for data streams
ACM SIGKDD Explorations Newsletter
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The central challenge in temporal data analysis is to obtain knowledge about its underlying dynamics. In this paper, we address the observation of noisy, stochastic processes and attempt to detect temporal segments that are related to inconsistencies and irregularities in its dynamics. Many conventional anomaly detection approaches detect anomalies based on the distance between patterns, and often provide only limited intuition about the generative process of the anomalies. Meanwhile, model-based approaches have difficulty in identifying a small, clustered set of anomalies. We propose Information-theoretic Meta-clustering (ITMC), a formalization of model-based clustering principled by the theory of lossy data compression. ITMC identifies a 'unique' cluster whose distribution diverges significantly from the entire dataset. Furthermore, ITMC employs a regularization term derived from the preference for high compression rate, which is critical to the precision of detection. For empirical evaluation, we apply ITMC to two temporal anomaly detection tasks. Datasets are taken from generative processes involving heterogeneous and inconsistent dynamics. A comparison to baseline methods shows that the proposed algorithm detects segments from irregular states with significantly high precision and recall.