Introduction to expert systems
Introduction to expert systems
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Algorithms for finding patterns in strings
Handbook of theoretical computer science (vol. A)
Neural computation and self-organizing maps: an introduction
Neural computation and self-organizing maps: an introduction
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Neural networks: a systematic introduction
Neural networks: a systematic introduction
A framework for knowledge-based temporal abstraction
Artificial Intelligence
Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying distinctive subsequences in multivariate time series by clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural, Novel and Hybrid Algorithms for Time Series Prediction
Neural, Novel and Hybrid Algorithms for Time Series Prediction
Self-Organizing Maps
Intelligent Hybrid Systems
A Method for Temporal Knowledge Conversion
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Evaluation of Automatic and Manual Knowledge Acquisition for Cerebrospinal Fluid (CSF) Diagnosis
AIME '97 Proceedings of the 6th Conference on Artificial Intelligence in Medicine in Europe
Local Learning for Iterated Time-Series Prediction
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Syntactic recognition of ECG signals by attributed finite automata
Pattern Recognition
Incorporating background knowledge for better prediction of cycle phases
Knowledge and Information Systems
Data mining with Temporal Abstractions: learning rules from time series
Data Mining and Knowledge Discovery
A taxonomy of Self-organizing Maps for temporal sequence processing
Intelligent Data Analysis
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
A Human-Machine Cooperative Approach for Time Series Data Interpretation
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Intelligent adaptive monitoring for cardiac surveillance
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
A Temporal Data Mining Approach for Discovering Knowledge on the Changes of the Patient's Physiology
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Temporal data mining for the quality assessment of hemodialysis services
Artificial Intelligence in Medicine
A review on time series data mining
Engineering Applications of Artificial Intelligence
Change detection with Kalman filter and CUSUM
Ubiquitous knowledge discovery
Change detection with Kalman filter and CUSUM
Ubiquitous knowledge discovery
Change detection with kalman filter and CUSUM
DS'06 Proceedings of the 9th international conference on Discovery Science
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This paper presents a method for the discovery of temporal patterns in multivariate time series and their conversion into a linguistic knowledge representation applied to sleep-related breathing disorders. The main idea lies in introducing several abstraction levels that allow a step-wise identification of temporal patterns. Self-organizing neural networks are used to discover elementary patterns in the time series. Machine learning (ML) algorithms use the results of the neural networks to automatically generate a rule-based description. At the next levels, temporal grammatical rules are inferred. This method covers one of the main ''bottlenecks'' in the design of knowledge-based systems, namely, the knowledge acquisition problem. An evaluation of the rules lead to an overall sensitivity of 0.762, and a specificity of 0.758.