Feature generation for sequence categorization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A monothetic clustering method
Pattern Recognition Letters
A smart room for hospitalised elderly people essay of modelling and first steps of an experiment
Technology and Health Care
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Scalable Feature Mining for Sequential Data
IEEE Intelligent Systems
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Feature-based classification of time-series data
Information processing and technology
Similarity Search for Multidimensional Data Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Modified Gath--Geva clustering for fuzzy segmentation of multivariate time-series
Fuzzy Sets and Systems
Artificial Intelligence in Medicine
A multidimensional temporal abstractive data mining framework
HIKM '10 Proceedings of the Fourth Australasian Workshop on Health Informatics and Knowledge Management - Volume 108
A disk-aware algorithm for time series motif discovery
Data Mining and Knowledge Discovery
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
A decision-making mechanism for context inference in pervasive healthcare environments
Decision Support Systems
Finding recurrent patterns from continuous sign language sentences for automated extraction of signs
The Journal of Machine Learning Research
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Objective: For the last years, time-series mining has become a challenging issue for researchers. An important application lies in most monitoring purposes, which require analyzing large sets of time-series for learning usual patterns. Any deviation from this learned profile is then considered as an unexpected situation. Moreover, complex applications may involve the temporal study of several heterogeneous parameters. In that paper, we propose a method for mining heterogeneous multivariate time-series for learning meaningful patterns. Methods: The proposed approach allows for mixed time-series - containing both pattern and non-pattern data - such as for imprecise matches, outliers, stretching and global translating of patterns instances in time. Results: We present the results of our approach on synthetic data generated in the context of monitoring a person at home, as well as early results on few real sequences. The purpose is to build a behavioral profile of a person in their daily activities by analyzing the time variations of several quantitative or qualitative parameters recorded through a provision of sensors. Conclusions: The results are very promising. They also highlight the difficulty of tuning the parameters of the method.