Predictive Data Mining: Practical Examples

The paper addresses some theoretical and practical aspects of data mining, focusing on predictive data mining, where two central types of prediction problems are discussed: classification and regression. Further accent is made on predictive data mining, where the time-stamped data greatly increase the dimensions and complexity of problem solving. The main goal is through processing of data (records from the past) to describe the underlying dynamics of the complex systems and predict its future. Second part of the paper briefly highlights the methodologies for predictive data mining used in this paper, namely: Bayesian classifier, decision tree induction algorithm (C4.5) and 'local' modelling using chaos theory. Last part of the paper presents application of the predictive data mining techniques to hydro-meteorological data.