Rough Set Theory And An Applıcatıon On Traffıc Accıdents
One of the questions that today’s technology tries to look for the answer is how data mining in large masses is organized and analyzed. Data mines stored in the digital space cover a very important place for companies and institutions. The decisions that are made by using this data give the possibility to offer of scientific reasons and analytical accuracy of the decision to organizations. Data analysis, data mining or knowledge discovery from data stack have a vital importance in terms of enhancing the competitiveness. After analysis of the data to determine if data is meaningful is another phase of data mining. In this context, a lot of studies and applications on the discovery and analysis of meaningful data from the database will be seen when literature is reviewed. These studies are known as data mining.
In this study, after data mining and information system were explained by Rough Set Theory, relationship between other approaches is discussed. In the second chapter, cons and Profs of Rough Set Theory are mentioned. In the third chapter, traffic accident data taken from various states of the United States is analyzed. In the last chapter the decision rules as a result of this study are interpreted.
