It is not always possible to solve a large size of data via traditional statistical techniques. In order to solve these kinds of data special tactics like data mining are needed. Data mining may meet these kinds of needs with both categorizing and piling tactic. In this study, we have used data mining by using Rural Development Investment Support Program (RDISP) data with various categorizing algorithms. The most prospering categorizing algorithm was tried to determine by using present data. At the end of analysis, it has been understood that MLP (multilayer perceptron), a nerve net model, is the best algorithm that makes the best categorizing. Keywords: Data mining, MLP, Nerve net model, RDISP, Rural development Kırsal Kalkınma Yatırımlarının Desteklenmesi Programı Verileri Kullanılarak Seçilen Algoritmalarının Sınıflandırma Performanslarının Karşılaştırılması ÖzetKapsamlı verileri geleneksel istatistiksel teknikler yardımıyla değerlendirmek mümkün değildir. Bu tür kapsamlı verileri değerlendirmek için "Veri Madenciliği" gibi özel tekniklere ihtiyaç vardır. Veri madenciliği kapsamlı verileri hem sınıflandırarak hem de kümeleyerek değerlendirmeyi kolaylaştırmaktadır. Bu çalışmada, Kırsal Kalkınma Yatırım Destekleme Programı (KKYDP) verilerinde çeşitli kategorize algoritmaları yardımıyla veri madenciliği tekniği kullanılmıştır. Çalışmada en uygun kategorize algoritma mevcut veriler kullanarak belirlenmeye çalışılmıştır. Sonuç olarak; analizlerde en iyi kategorizasyon yapan algoritma modelinin Çok Katmanlı Algılayıcı (ÇKA) yapay sinir ağ modeli olduğu belirlenmiştir.

Aim: Data mining is an interdisciplinary field, constantly developing and expanding its use. It helps to ensure the reliability of data by using various techniques and algorithms. Classification is an important data mining technique because it is widely used by researchers. Method: In this study, the classification results of SMO and J48 algorithms were compared on the data of three arc students. The performance of J48 and SMO algorithms in terms of classification accuracy was evaluated using three different data sets and various accuracy measurements such as TP-Rate, FP-Ratio, Precision, Precision, F-criterion and ROC analysis. Findings and Results: As a result of the tests, it was revealed that the classification performance of SMO algorithm was better in all three datasets.

Data mining is a significant method which is utilized in order to reveal the hidden patterns and connections within big data. The method is used at various fields such as financial transactions, banking, education, health sector, logistics and security. Even though analysis towards the consumption habits of the customers is carried out via association rules mining more often, which is one of the basic methods of data mining, the method is also utilized in order to profile patients and students. As well as the customization of a customer is of high significance, so is distinguishing and customizing a student. Within this study, students were tried to be profiled via data mining of the student data of a high school. A set of qualities, that can directly affect the performance of students such as health conditions, financial resources, life standards and education level of the families, were taken into consideration. For that purpose, upon the analysis of data of 443 students in the database, a data warehouse was established. The Apriori algorithm, which is one of the popular algorithms of association rules mining, is utilized for the data analysis. Apriori algorithm was able to produce 72 rules which are accurate above 90%. It is thought that the produced rules can be of help in profiling the students, and they can contribute to work of school management, teachers, parents and students.

One of the most important strategic decisions taken by company managers at corporate level is in which areas to invest and how to manage and organize investments. Strategic management proposes portfolio analysis techniques in this regard. It is stated that portfolio analysis techniques, which have been criticized at many points, should be considered as initial techniques and should be supported by other techniques in practice. At this point, the question of whether or not data mining techniques will be used to identify new investment areas and allocate resources has constituted the main problematic of this study. In this study, it has been investigated whether there are association rules between investment areas thus, it is tried to reach the evaluations that may affect the strategic decisions in the process of determining investment areas, by using data belonging to various investor organizations. For this purpose, Association Rules Mining was conducted using data from 102 holding companies. As a result of the study, 35 rules were produced above the 50% confidence level. It is presented as a suggestion to the enterprises in which they can benefit from these rules in their investment planning.

In this study, it was aimed to investigate whether an association rule exists between the products sold, using the sales data of a supermarket with the data mining method within the framework of a customer-oriented approach. For this purpose, the Association Rule Mining Method was used, and analyses were carried out on existing data with the Apriori Algorithm that is widely used in this method. Various association rules were determined between the products sold as a result of these analyses. It was assessed that Association Rule Mining is an alternative technique to proactive customer orientation by revealing the latent purchasing behaviour patterns of the customers.