Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Mining the stock market (extended abstract): which measure is best?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Multidimensional binary search trees used for associative searching
Communications of the ACM
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Similarity Queries for Time-Series Data: Constraint Specification and Implementation
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
Mining Motifs in Massive Time Series Databases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Unfolding preprocessing for meaningful time series clustering
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Useful clustering outcomes from meaningful time series clustering
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Clustering multidimensional sequences in spatial and temporal databases
Knowledge and Information Systems
Establishing relationships among patterns in stock market data
Data & Knowledge Engineering
Subspace sums for extracting non-random data from massive noise
Knowledge and Information Systems
Discovering multivariate motifs using subsequence density estimation and greedy mixture learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Data mining of vector–item patterns using neighborhood histograms
Knowledge and Information Systems
Point-distribution algorithm for mining vector-item patterns
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
A review on time series data mining
Engineering Applications of Artificial Intelligence
Data Mining and Knowledge Discovery
Discovering deformable motifs in continuous time series data
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
ACM Computing Surveys (CSUR)
Finding recurrent patterns from continuous sign language sentences for automated extraction of signs
The Journal of Machine Learning Research
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Noise levels in time series subsequence data are typically very high, and properties of the noise differ from those of white noise. The proposed algorithm incorporates a continuous random-walk noise model into kernel-density-based clustering. Evaluation is done by testing to what extent the resulting clusters are predictive of the process that generated the time series. It is shown that the new algorithm not only outperforms partitioning techniques that lead to trivial and unsatisfactory results under the given quality measure, but also improves upon other density-based algorithms. The results suggest that the noise elimination properties of kernel-density-based clustering algorithms can be of significant value for the use of clustering in preprocessing of data.