C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data preparation for data mining
Data preparation for data mining
Discovering knowledge from low-quality meterological databases
Knowledge discovery and data mining
A meteorological knowledge-discovery environment
Knowledge discovery and data mining
Knowledge Discovery in Databases
Knowledge Discovery in Databases
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Optimizations of the Combinatorial Neural Model
SBRN '98 Proceedings of the Vth Brazilian Symposium on Neural Networks
Evaluation of Sampling for Data Mining of Association Rules
Evaluation of Sampling for Data Mining of Association Rules
Learning in the combinatorial neural model
IEEE Transactions on Neural Networks
Meteorological phenomena forecast using data mining prediction methods
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
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In many application areas there are large historical data containing useful knowledge for decision support. However, this data taken in its raw form is usually of a poor quality. Thus it has very little value for the user-decisionmaker if not adequately prepared. The Knowledge Discovery in Databases (KDD) is concerned with exploiting massive data sets in supporting use of historical data for decision-making. This paper describes an ongoing research project in the context of meteorological aviation forecasting, concerned with fog forecasting. The paper discusses the stages for performing knowledge discovery in the meteorological aviation-forecasting domain. The data used for research was taken from a real data set describing the aviation weather observations. The paper presents the data pre-processing stage, the discovered rules, achieved results and further directions of such research. We believe that this project can serve as a model for in a wider KDD-based decision support problem.