Öz
Kaynakça
- [1] M. Kutlu, C. Küçüközmen, F. Çınar, Büyük Veri Analizi, Görselleştirme ve Risk Yönetimi, 12 Şubat 2015. [Online]. Available: https://tr.linkedin.com/pulse/d%C3%BCnya-dillerine-en-son-ve-bir-yenisi-olarak-r-dili-analitik-%C3%A7inar.
- [2] Y. Özkan. Veri Madenciliği Yöntemleri, Türkiye: Papatya yayıncılık, 2013.
- [3] J. Han, M. Kamber, Data Mining: Concepts and Techniques, USA: Morgan Kaufmann Publisher, 2000.
- [4] G.S. Gordon M. Berry. Mastering data mining, USA: John Willey and Sons, 2000.
- [5] L.B. Ayre, Data Mining For Information Professionals, San Diego, California: USA, 2006.
- [6] E. Alpaydın, “Ham veriden altın bilgiye ulaşma yöntemleri,” Bilişim 2000 Eğitim Semineri, İstanbul, Türkiye, 2000.
- [7] Anonymous, Veri Görselleştirme Nedir,, 15 Mart 2016. [Online]. Available: http://www.verigorsellestirme.com/veri-gorsellestirme-nedir/
- [8] S. E. Seker, “Customer churn analysis”, YBS Ansiklopedi, Vol. 3, No. 1, 2016.
- [9] W. Hadley, C. Winston, KMggplot2, 30 Aralık 2016. [Online]. Access: https://cran.r-project.org/ packages/RcmdrPlugin.KMggplot2.
Data Mining Through Data Visualization: A Case Study on Predicting Churners on Telecomunications Data Set
Öz
Data mining is the
process of extracting meaningful information from a large, raw data. These
processes are carried out by various, detailed methods. And, the obtained
results are used to make various interpretations and to draw conclusions.
Deductions can either be made by interpreting the data after various operations
or by plotting the data in various forms of graphs. This type of interpretation
over graphics is called data mining through data visualization. Generating
graphs that can be used to draw various conclusions on a telecommunications
data set with the help of some packages included in the R program is presented
in the paper. It does not require upper-level math skills to interpret these
graphics; and everyone having knowledge about the industry and data set of the
graphs has the ability to plot similar graphs and make analysis and
interpretations regarding the results obtained on the data set at hand. In this
study, R language was preferred as the software infrastructure for data mining
applications, and graphs were plotted for interpretation through data
visualization with data mining.
Anahtar Kelimeler
Kaynakça
- [1] M. Kutlu, C. Küçüközmen, F. Çınar, Büyük Veri Analizi, Görselleştirme ve Risk Yönetimi, 12 Şubat 2015. [Online]. Available: https://tr.linkedin.com/pulse/d%C3%BCnya-dillerine-en-son-ve-bir-yenisi-olarak-r-dili-analitik-%C3%A7inar.
- [2] Y. Özkan. Veri Madenciliği Yöntemleri, Türkiye: Papatya yayıncılık, 2013.
- [3] J. Han, M. Kamber, Data Mining: Concepts and Techniques, USA: Morgan Kaufmann Publisher, 2000.
- [4] G.S. Gordon M. Berry. Mastering data mining, USA: John Willey and Sons, 2000.
- [5] L.B. Ayre, Data Mining For Information Professionals, San Diego, California: USA, 2006.
- [6] E. Alpaydın, “Ham veriden altın bilgiye ulaşma yöntemleri,” Bilişim 2000 Eğitim Semineri, İstanbul, Türkiye, 2000.
- [7] Anonymous, Veri Görselleştirme Nedir,, 15 Mart 2016. [Online]. Available: http://www.verigorsellestirme.com/veri-gorsellestirme-nedir/
- [8] S. E. Seker, “Customer churn analysis”, YBS Ansiklopedi, Vol. 3, No. 1, 2016.
- [9] W. Hadley, C. Winston, KMggplot2, 30 Aralık 2016. [Online]. Access: https://cran.r-project.org/ packages/RcmdrPlugin.KMggplot2.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Yayımlanma Tarihi | 1 Nisan 2018 |
| Yayımlandığı Sayı | Yıl 2018 Cilt: 6 |
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