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Analyzing Factors Affecting the Performance of Data Mining Tools

  • Balrajpreet KaurEmail author
  • Anil Sharma
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 712)

Abstract

Data mining is the key technique for finding interesting patterns and hidden information from huge volume of data. There is a wide range of tools available with different algorithms and techniques to work on data. These data mining tools provide a generalized platform for applying machine learning techniques on dataset to attain required results. These tools are available as open source as well as on payment mode which provide more customizable options. Every tool has its own strength and weakness, but there is no obvious consensus regarding the best one. This paper focuses on three tools namely WEKA, Orange and MATLAB. Authors compared these tools on some given factors like correctly classified accuracy, in-correctly classified accuracy and time by applying four algorithms i.e. Support Vector Machine (SVM), K Nearest Neighbour (KNN), Decision Tree and Naive Bayes for getting performance results with two different datasets.

Keywords

Data mining Data mining tools WEKA Orange MATLAB SVM KNN Decision tree Naive Bayes 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.School of Computer ApplicationLPUPhagwaraIndia

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