Karar Ağaçlarıyla Öğrenci Verilerinin Sınıflandırılması

Mehmet Alan

doi:10.16951/iibd.11519

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Yıl 2014, Cilt: 28 Sayı: 4, 0 - , 18.11.2014

Mehmet Alan

Öz

In this study, a data mining application was conducted using the data from students of Faculty of Economics and Administrative Sciences in Cumhuriyet University. Both decision tree algorithm which classifies the data best and classes produced by this algorithm were tried to determine by benefiting from the students’ data. As a result of the study, it is explored that LADTree algorithm was the best algorithm which classifies the students’ data and nineteen classes were produced by this algorithm

Anahtar Kelimeler

Kaynakça

Albayrak, A.Sait ve Yılmaz, Şebnem (2009), Veri Madenciliği: Karar Ağacı Algoritmaları Ve İMKB Verileri Üzerine Bir Uygulama Süleyman Demirel Üniversitesi, İktisadi ve İdari Bilimler Fakültesi Dergisi, Cilt.14, Sayı.1, ss.31–52
Ayık, Y. Ziya, Özdemir, Abdülkadir ve Yavuz Uğur (2007), Lise Türü Ve Lise Mezuniyet Başarısının, Kazanılan Fakülte İle İlişkisinin Veri Madenciliği Tekniği İle Analizi,. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Cilt 10, Sayı 2, ss.441–454
Baykal, Abdullah ve Coşkun Cengiz (2011), Veri Madenciliğinde Sınıflandırma Karşılaştırılması, http://ab.org.tr/ab11/bildiri/67.pdf, 18.01.2013
Bose, I., Chun, L. A.,Yue, L. V. W., Ines, L. H. W. and Helen, W. O. L., (2009), Business Data Warehouse: The Case of Wal-Mart, Data Mining Applications for Empowering Knowledge Societies, Ed. Hakikur Rahman, Information Science Reference, pp.189-198
Bramer, Max (2007), Principles of Data Mining, Springer, London
Dener, Murat, Dörerler Murat ve Orman Abdullah, (2009), Açık Kaynak Kodlu Veri Madenciliği Programları: WEKA’da Örnek Uygulama, ab.org.tr/ab09/bildiri/42.pdf, 18.01.2013
Dolgun ve Diğerleri (2009, Veri Madenciliğinde Yapısal Olmayan Verilerin Analizi: Metin ve Web Madenciliği, İstatistikçiler Dergisi 2, ss.48-58
Dong-Peng Yang, Li Jin-Lin, Lun Ran and Chao Zhou, (2008), Applications of Data Mining Methods in the Evaluation of Client Credibility, Applications of Data Mining in E-Business and Finance C. Soares et al. (Eds.), IOS Press, Amsterdam, pp.35-43
Giudici, Paolo and Figini, Silvia, (2009), Applied Data Mining For Business and Industry, Second Edition, Wiley Publicition, West Sussex
Han, Jiawei and Kamber, Micheline, (2006), Data Mining: Concepts and Techniques, Second Edition, Morgan Kaufmann Publications, San Francisco
Ivancsy, Renata and Vajk, Istvan, (2005), “Fast Discovery Of Frequent Itemsets: A Cubic Structure-Based Approach”, Informatica 29, pp.71– 78
Jain, Y. K., Yadav, V. K. and Panday, G. S., (2011), “An Efficient Association Rule Hiding Algorithm for Privacy Preserving Data Mining”, International Journal On Computer Science And Engineering, Vol. 3 No. 7, pp. 2792-2798.
Kantardzic, Mehmed , (2003). Data Mining: Concepts, Models, Methods, and Algorithms, John Wiley & Sons J. B. Speed Scientific School, University of Louisville IEEE Computer Society
Kaya Yılmaz, Ertuğrul Ö. Faruk ve Tekin Ramazan (2012), Batman University International participated Science and Culture Symposium, Batman University Journal of Life Sciences, Volume 1, Number 2, ss.403-413
Larose, Daniel T., (2005), Discovering Knowledge In Data, Wiley Publication, New Jersey.
Larose, Daniel T., (2006), Data Mining Methods and Models, A John Wiley & Sons, Inc., Publication, New Jersey
Nisbet, R., Elder, J., and Miner, G., (2009), Handbook of Statistical Analysis and Data Mining Applications, Elsevier Inc, Burlington.
Özkan, Yalçın (2008), Veri Madenciliği Yöntemleri, Papatya Yayınları, İstanbul
Rokach, Lior and Maimon, Oded (2008), Data Mining with Decision Trees, World Scientific, New Jersey
Tadesse, T., Wardlow, B. And Hayes, M.J. (2009), The Application of Data Mining for Drought Monitoring and Prediction, Data Mining Applications
for Empowering Knowledge Societies, Edited by Hakikur Rahman, Information Science Reference, New York, pp.280-291
Silahtaroğlu, Gökhan (2008), Veri Madenciliği, Papatya Yayınları, İstanbul
Wang, Chien-Hua and Lee, Wei-Hsuan & Pang, Chin-Tzong, (2010). “Applying Fuzzy FP-Growth To Mine Fuzzy Association Rules”, World Academy of Science, Engineering And Technology, 65, pp. 956-962
Wu, Tong and Li Xiangyang (2003), Data Storage and Management, The Handbook of Data Mining, Edited by. Nong Ye, Lawrence Erlbaum Associates Publishers. London, pp.393-407
Weiss, Sholom M. And Zhang, Tong (2003), Performance Analysis and Evaluation, The Handbook of Data Mining, Edited by. Nong Ye, Lawrence Erlbaum Associates Publishers. London, pp.436-439