Using Flow Graphs In Data Mining

Databases are widely used in data processes and each day their sizes are getting larger. In order to access to the data stored in growing databases and to use them, new techniques are developed to discover the knowledge automatically. Data mining techniques may be used to find the useful knowledge with analyzing and discovering the data. Data mining is the search for the relations and the rules, which help us to make estimations about the future from large-scale databases, using computer programs. Data mining is a process that uses the existing technology and acts as a bridge between data and logical decision-making. The knowledge discovery from the databases is the determination of different patterns, and defining them in a meaningful, short and unique manner. Knowledge discovery allows using necessary systematical data to obtain the useful patterns from a large database. Knowledge discovery for decision-making processes and market estimations plays an important role in supplying necessary information to business in databases. There are various methods that have been used in data mining such as support vector machines, artificial neural networks, decision trees, genetic algorithms, Bayesian networks, flow graphs etc. Flow graphs proposed by Pawlak are efficient and useful graphical tools that are used in data mining in order to analyze and represent knowledge. In this study; the mathematical background of flow graphs that was proposed by Pawlak will be examined and then an example will be given.