Data mining is a set of methods for data processing with the aim to obtain non-trivial information not apparent at first glance usually due to the huge data volume or their complexity. This new scientific discipline helps to solve problems of this kind. and Data mining, neboli dolování dat, je soubor metod sloužících ke zpracování dat a získání netriviálních informací v nich obsažených, které nejsou na první pohled zřejmé a ani zkušení odborníci je nejsou schopni odhalit, zejména z důvodu velikosti datových souborů nebo komplexnosti vazeb. Proto vznikl data mining jako vědní obor, který za pomoci moderní výpočetní techniky řeší podobné problémy.
Population fluctuations of the well-known oak defoliator, the oak processionary moth (Thaumetopoea processionea L.), were studied using light trap data and basic meteorological parameters (monthly average temperatures, and precipitation) at three locations in Western Hungary over a period of 15 years (1988-2012). The fluctuations in the numbers caught by the three traps were strongly synchronized. One possible explanation for this synchrony may be similar weather at the three trapping locations. Cyclic Reverse Moving Interval Techniques (CReMIT) were used to define the period of time in a year that most strongly influences the catches. For this period, we defined a species specific aridity index for Thaumetopoea processionea (THAU-index). This index explains 54.8-68.9% of the variation in the yearly catches, which indicates that aridity, particularly in the May-July period was the major determinant of population fluctuations. Our results predict an increasing future risk of Oak Processionary Moth (OPM) outbreaks and further spread if the frequency of severe spring/summer droughts increases with global warming., György Csóka, Anikó Hirka, Levente Szöcs, Norbert Móricz, Ervin Rasztovits, Zoltán Pödör., and Obsahuje bibliografii