C4.5: programs for machine learning
C4.5: programs for machine learning
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Speech recognition with dynamic Bayesian networks
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Efficient Retrieval of Similar Time Sequences Under Time Warping
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Learning Comprehensible Descriptions of Multivariate Time Series
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A Signature Technique for Similarity-Based Queries
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Supervised classification with temporal data
Supervised classification with temporal data
Clustering of streaming time series is meaningless
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
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
Making clustering in delay-vector space meaningful
Knowledge and Information Systems
Stochastic processes and temporal data mining
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
ICCS'03 Proceedings of the 1st international conference on Computational science: PartI
A review on time series data mining
Engineering Applications of Artificial Intelligence
Short communication: Selective Subsequence Time Series clustering
Knowledge-Based Systems
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Due to the wide availability of huge data collection comprising multiple sequences that evolve over time, the process of adapting the classical data-mining techniques, making them capable to work into this new context, becomes today a strong necessity. In [1] we proposed a methodology permitting the application of a classification tree on sequential raw data and the extraction of the rules having a temporal dimension. In this article, we propose a formalism based on temporal first logic-order and we review the main steps of the methodology through this theoretical frame. Finally, we present some solutions for a practical implementation.