Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Finding recurrent sources in sequences
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Learning Comprehensible Descriptions of Multivariate Time Series
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning First Order Logic Time Series Classifiers: Rules and Boosting
PKDD '00 Proceedings of the 4th European Conference on Principles of 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
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Sequential Association Rule Mining with Time Lags
Journal of Intelligent Information Systems
Extracting interpretable muscle activation patterns with time series knowledge mining
International Journal of Knowledge-based and Intelligent Engineering Systems
Algorithms for time series knowledge mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Efficient mining of understandable patterns from multivariate interval time series
Data Mining and Knowledge Discovery
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
Establishing relationships among patterns in stock market data
Data & Knowledge Engineering
Discretization of Time Series Dataset with a Genetic Search
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
On privacy in time series data mining
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Segmentation and classification of time-series: real case studies
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
A new class of attacks on time series data mining\m{1}
Intelligent Data Analysis
Pattern Recognition and Image Analysis
A review on time series data mining
Engineering Applications of Artificial Intelligence
Discretization of time series dataset using relative frequency and K-nearest neighbor approach
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Improving the classification accuracy of streaming data using SAX similarity features
Pattern Recognition Letters
A multi-hierarchical representation for similarity measurement of time series
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Times series discretization using evolutionary programming
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Behavior pattern recognition in electric power consumption series using data mining tools
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
A symbolic representation method to preserve the characteristic slope of time series
SBIA'12 Proceedings of the 21st Brazilian conference on Advances in Artificial Intelligence
Preserving Privacy in Time Series Data Mining
International Journal of Data Warehousing and Mining
mDBN: motif based learning of gene regulatory networks using dynamic bayesian networks
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Knowledge Discovery in time series usually requires symbolic time series. Many discretization methods that convert numeric time series to symbolic time series ignore the temporal order of values. This often leads to symbols that do not correspond to states of the process generating the time series and cannot be interpreted meaningfully. We propose a new method for meaningful unsupervised discretization of numeric time series called Persist. The algorithm is based on the Kullback-Leibler divergence between the marginal and the self-transition probability distributions of the discretization symbols. Its performance is evaluated on both artificial and real life data in comparison to the most common discretization methods. Persist achieves significantly higher accuracy than existing static methods and is robust against noise. It also outperforms Hidden Markov Models for all but very simple cases.