BibTex RIS Cite

-

Year 2014, Volume: 28 Issue: 4, 0 - , 18.11.2014

Abstract

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

References

  • 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

Karar Ağaçlarıyla Öğrenci Verilerinin Sınıflandırılması

Year 2014, Volume: 28 Issue: 4, 0 - , 18.11.2014

Abstract

Bu çalışmada, karar ağaçları yöntemiyle Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Fakültesi öğrencilerine ait veriler kullanılarak veri madenciliği yapılmıştır. Öğrencilere ait verilerden yararlanarak, hem bu verileri en başarılı sınıflandıran karar ağaçlarına ait algoritma, hem de bu algoritmanın üreteceği sınıflar tespit edilmeye çalışılmıştır. Çalışmanın sonucunda LADTree algoritmasının öğrenci verilerini sınıflandırmada en başarılı algoritma olduğu ve ondokuz değişik sınıf ürettiği anlaşılmıştır.

References

  • 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
There are 25 citations in total.

Details

Primary Language Turkish
Journal Section Makaleler
Authors

Mehmet Alan

Publication Date November 18, 2014
Published in Issue Year 2014 Volume: 28 Issue: 4

Cite

APA Alan, M. (2014). Karar Ağaçlarıyla Öğrenci Verilerinin Sınıflandırılması. Atatürk Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 28(4). https://doi.org/10.16951/iibd.11519

4aoDA4.pngithenticate-badge-rec-positive.png800px-Open-Access-PLoS.svg.png