VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Generating English summaries of time series data using the Gricean maxims
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Elicitation of fuzzy association rules from positive and negative examples
Fuzzy Sets and Systems
Perception-based approach to time series data mining
Applied Soft Computing
Intelligent agents for real time data mining in telecommunications networks
AIS-ADM'07 Proceedings of the 2nd international conference on Autonomous intelligent systems: agents and data mining
Time series pattern recognition based on MAP transform and local trend associations
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Hi-index | 0.00 |
Import of intelligent features to time series analysis including the possibility of operating with linguistic information, reasoning and replying on intelligent queries is the prospective direction of development of such systems. The paper proposes novel methods of perception based time series data mining using perceptual patterns, fuzzy rules and linguistic descriptions. The methods of perception based forecasting using perceptual trends and moving approximation (MAP) transform are discussed. The first method uses perception based function for modeling qualitative forecasting given by expert judgments. The second method uses MAP transform and measure of local trend associations for description of perceptual pattern corresponding to the region of forecasting. Finally, the method of generation of association rules for multivariate time series based on MAP and fuzzy trends is discussed. Multivariate time series are considered as description of system dynamics. In this case association rules can be considered as relationships between system elements additional to spatial, causal etc. relations existing in the system. The proposed methods are illustrated on examples of artificial and real time series.