Data Mining Through Data Visualization: A Case Study on Predicting Churners on Telecomunications Data Set


Başarslan M. S. , Kayaalp F.

Balkan Journal of Electrical and Computer Engineering, vol.6, pp.42-45, 2018 (Peer-Reviewed Journal)

  • Publication Type: Article / Article
  • Volume: 6
  • Publication Date: 2018
  • Doi Number: 10.17694/bajece.410243
  • Journal Name: Balkan Journal of Electrical and Computer Engineering
  • Journal Indexes: TR DİZİN (ULAKBİM)
  • Page Numbers: pp.42-45

Abstract

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.

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.