Application of a novel algorithm with data clustering tree method for categorical variables

The major reason that data mining became one of the hottest current technologies of the information age is the wide availability of huge amounts of data and the need for turning such data into useful information and knowledge. As computer systems getting cheaper and computer power increases, the amount of data available to be collected and processed increases. Therefore using techniques that operates very well with large amounts of data becomes an obvious choice. The information and knowledge gained can be used for applications ranging from business management, production control, and market analysis, to engineering design and science exploration.In this study, a new data mining algorithm used and tested for categorical variable. This algorithm improved by Yrd. Doç. Dr. Gökhan SİLAHTAROĞLU. This algorithm to call "A Tree Approach to Clustering Data with Categorical Variables". In the literature there are different approaches to form tree. To determine the best attribute, used an equal-split parameter. After forming the clusters, used another clustering algorithm such as PAM, CLARA or K-Means to reduce the number of leaves to the number required by the user.