%0 Journal Article
%T COMPARISON OF CLASSIFICATION RESULTS OF SMO AND J48 ALGORITHMS ON DIFFERENT DATA SETS
%A Cavit YE£¿£¿LYURT
%A Mehmet Ali ALAN
%J -
%D 2018
%X The data sources of institutions, social media shares, articles on websites and forms provide large amounts of data. It is very difficult to process large amounts of data in traditional ways and to produce information for use in decision processes. In this context, data mining can provide the production of the information needed from the available data with the advanced techniques that it offers. Databases are rich in confidential information that will enable rational decision-making. Classification and estimation are two important data analysis techniques used for estimating future data trends or explaining important data classes. These analyzes can be useful in better understanding of large amounts of data. Today, institutions produce large amounts of data, but they have difficulties in revealing meaningful and useful information within these data. It is not easy to analyze large data with traditional statistical methods. Special methods are therefore required to process and analyze data. Data mining methods have emerged to meet this requirement. The aim of this study is to compare the performances of the SMO and J48 algorithms used in the classification of data mining. For this purpose, data mining was performed by using three different student data sets. Data mining is an analysis method that summarizes data and exposes hidden relationships with both useful and understandable data, in unusual ways. This method is one of the processes of knowledge discovery in the database, which first explores scientific and technical data to reveal unknown patterns. Classification is a process that is frequently used in daily life. By classification, the objects are split and separated, that is, each of the mutually exclusive or general categories can be assigned as a class. Many practical decision-making processes can be formulated as a classification problem. For example, people or objects can be one of many categories. Classification is the process of assigning different elements in different classes. These classes may be business rules, class boundaries, or some mathematical functions. The classification process can be constructed on a relationship between a class of the classified element and a known class value and properties. This type of classification is called ¡°supervised learning¡±. If there are no known examples of a class, this classification is unsupervised. The most common uncontrolled classification approach is clustering. The most common applications of clustering technology are retail basket analysis and fraud detection. The concept of controlled
%K Veri Madencili£¿i
%K S£¿n£¿fland£¿rma
%K SMO
%K j48
%U http://dergipark.org.tr/jobs/issue/41643/487388