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The aim of this study was to verify whether and which parameters of the atmospheric pollen season can distinguish between pollen types, the ranges of parameter values that delineate classes of taxa, and finally which taxa are similar to others within the domain of these parameter ranges. Decision tree algorithms were applied and the best tree was chosen to describe the rules of pollen classification. The study material consisted of airborne pollen grains of the following eight taxa: Alnus, Betula, Carpinus, Corylus, Cupressaceae, Fraxinus, Populus and Ulmus. Research was conducted in Lublin in eastern Poland during 2001-2013. The following six atmospheric pollen season parameters were analyzed: season start and end, duration, maximum daily pollen concentration, date of maximum pollen concentration, and the Seasonal Pollen Index (SPI). Four algorithms were used in data analysis and the J4.8 algorithm was chosen as the best for taxa classification, date of the end of season and the SPI value belonging to characteristics that served most to discriminate between pollen types. Based on the classification tree, the following four groups of taxa were identified: (i) Ulmus; (ii) Corylus, Alnus, Populus; (iii) Betula; and (iv) Carpinus, Fraxinus, Cupressaceae.

Highlights: [•] We propose CT4RDD, a novel methodology for analysis and modeling of digital divide. [•] CT4RDD is based on C4.5, a reputed classification tree algorithm from the AI domain. [•] Data from the 2010 Mexican Census are used to create and evaluate our methodology. [•] Internet presence in households of cities is represented as a discrete value scale. [•] CT4RDD has produced if-then rules describing profiles of digital divide of cities. [ABSTRACT FROM AUTHOR]