Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The concept of LFLC 2000: its specificity, realization and power of applications
Computers in Industry
Fuzzy modifiers based on fuzzy relations
Information Sciences—Informatics and Computer Science: An International Journal
Precisiated natural language (PNL)
AI Magazine
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Mining pure linguistic associations from numerical data
International Journal of Approximate Reasoning
A comprehensive theory of trichotomous evaluative linguistic expressions
Fuzzy Sets and Systems
The GUHA method and its meaning for data mining
Journal of Computer and System Sciences
Examples, counterexamples, and measuring fuzzy associations
Fuzzy Sets and Systems
A linguistic approach to time series modeling with the help of F-transform
Fuzzy Sets and Systems
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Floods belong to the most hazardous natural disasters and their disaster management heavily relies on precise forecasts. These forecasts are provided by physical models based on differential equations. However, these models do depend on unreliable inputs such as measurements or parameter estimations which causes undesirable inaccuracies. Thus, an appropriate data-mining analysis of the physical model and its precision based on features that determine distinct situations seems to be helpful in adjusting the physical model. An application of fuzzy GUHA method in flood peak prediction is presented. Measured water flow rate data from a system for flood predictions were used in order to mine fuzzy association rules expressed in natural language. The provided data was firstly extended by a generation of artificial variables (features). The resulting variables were later on translated into fuzzy GUHA tables with help of Evaluative Linguistic Expressions in order to mine associations. The found associations were interpreted as fuzzy IF-THEN rules and used jointly with the Perception-based Logical Deduction inference method to predict expected time shift of flow rate peaks forecasted by the given physical model. Results obtained from this adjusted model were statistically evaluated and the improvement in the forecasting accuracy was confirmed.