Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering similar patterns in time series
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Time series similarity measures (tutorial PM-2)
Tutorial notes of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Infominer: mining surprising periodic patterns
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Motifs in Massive Time Series Databases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Mining an optimal prototype from a periodic time series: an evolutionary computation-based approach
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A disk-aware algorithm for time series motif discovery
Data Mining and Knowledge Discovery
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Significant motifs in time series
Statistical Analysis and Data Mining
Testing the significance of spatio-temporal teleconnection patterns
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
Discovering time series motifs based on multidimensional index and early abandoning
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Classifying heart sounds using multiresolution time series motifs: an exploratory study
Proceedings of the International C* Conference on Computer Science and Software Engineering
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The problem of discovering previously unknown frequent patterns in time series, also called motifs, has been recently introduced. A motif is a subseries pattern that appears a significant number of times. Results demonstrate that motifs may provide valuable insights about the data and have a wide range of applications in data mining tasks. The main motivation for this study was the need to mine time series data from protein folding/unfolding simulations. We propose an algorithm that extracts approximate motifs, i.e. motifs that capture portions of time series with a similar and eventually symmetric behavior. Preliminary results on the analysis of protein unfolding data support this proposal as a valuable tool. Additional experiments demonstrate that the application of utility of our algorithm is not limited to this particular problem. Rather it can be an interesting tool to be applied in many real world problems.