Humankind technological evolution has made a\ngreat breakthrough after the invention and widespread adoption of the printing\npress which facilitate gathering of data in a paper format. Currently we are\nexperiencing another, much greater information revolution as a consequence of advancing\ndigitalisation of all aspects of human live. With each second, bytes of digital\ndata are produced by various devices and stored in the cloud. Our capabilities\nof interpreting such gigantic volumes of data are much smaller than our\nabilities to store such quantities of data. Data mining technique have evolved\nto become very successful data analysing tool, facilitating accurate methodology\nfor data interpretation and extraction previously unknown knowledge from vast\nquantity of data (Sang 2011). Rapid\ndata mining evolution is portrayed by Kurgan & Cios (2005) who\nobserved dynamic increase in published data mining scientific papers from about\n20 in year 1990 to 1270 by the year 1995. Depending on the type of data provided\nand general purpose of the analysis the data mining comprise number of various\nmethods. Maimon O. and Rokach L. (2005) summarize characteristics\nof prediction methods as technique which, by analysing existing complex\nrelations between the data, create model that is used to predict value in new sample\ndata. Authors have divided prediction methods in to two subdivisions: supervised\nand unsupervised methods. Supervised method differs by the presence of “dependent\nvariable” in the sample dataset which is used to determine association between\nthe various combination data and value of dependent variable. The relationship,\nonce recognized, is used to build the model that will be employed to predict\ndependent variable in new dataset. A typical example of prediction method with\nsupervised learning is classification method which categorizes data into\npredefined class label (Quaem, A. 2012).

Classification Research Paper