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)


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.


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


  1. 1.
    Rnageri, B.: Data mining techniques and applications. Indian J. Comput. Sci. Eng. (2010)Google Scholar
  2. 2.
    Agrawal, H., Agrawal, P.: Review on data mining tools. IJISET 1, 52–56 (2014)Google Scholar
  3. 3.
    Kumar, R., Verma, R.: Classification algorithm for data mining: a survey. IJIET 1, 7–14 (2012)Google Scholar
  4. 4.
    Joshi, A., Pandey, N., Chawla, R., Patil, P.: Use of data mining techniques to improve the effectiveness of sales and marketing. IJCSMC 4 (2015)Google Scholar
  5. 5.
    Patel, S., Desai, S.: A comparative study on data mining tools. IJATCSE (2015)Google Scholar
  6. 6.
    Krstevski, J., Mihajlove, D., Chorbev, I.: Student data analysis with rapid miner. In: ICT Innovations (2011)Google Scholar
  7. 7.
    Berthold, M., et al.: KNIME: the Konstanz Information Miner. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds.) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization, pp. 319–326. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-78246-9_38
  8. 8.
    Hall, M., Frank, E., Holmes, G.: The WEKA data mining software: an update. SIGKDD Explor. (2009)Google Scholar
  9. 9.
    Demšar, J., Zupan, B.: Orange: data mining fruitful and fun - a historical perspective (2012)Google Scholar
  10. 10.
    Loper, E., Bird, S.: NLTK: The Natural Language Toolkit (2002)Google Scholar
  11. 11.
    Hirudkar, A., Sherebar, S.: Comparative analysis of data mining tools and techniques for evaluating performance of database system. Int. J. Comput. Sci. Appl. (2013)Google Scholar
  12. 12.
    Lashari, A., Ibrahim, R.: Comparative analysis of data mining techniques for medical data classification. In: 4th International Conference on Computing and Information (2013)Google Scholar
  13. 13.
    Sharma, R., Kumar, S., Maheshwari R.: Comparative analysis of classification technique in data mining using different dataset. Int. J. Comput. Sci. Mob. Comput. (2015)Google Scholar
  14. 14.
    Joshi, S., Shetty, S.: Performance analysis of different classification methods in data mining for diabetes dataset using WEKA tool. IJRITCC 3, 1168–1173 (2015)Google Scholar
  15. 15.
    Singh, P., Grag, R., Singh, S., Singh, D.: Comparative study of data mining algorithm through WEKA. Int. J. Emerg. Res. Manag. Technol. (2015)Google Scholar
  16. 16.
    Patel, J.: Classification algorithm and comparison in data mining. Int. J. Innov. Adv. Comput. Sci. (2015)Google Scholar
  17. 17.
    Khan, A., Ahmed, S.: Comparative analysis of data mining tools for lung cancer patients. J. Inf. Commun. Technol. (2015)Google Scholar
  18. 18.
    Verma, A., Kaur, I., Arora, N.: Comparative analysis of information extraction techniques for data mining. Indian J. Comput. Sci. Technol. (2016)Google Scholar
  19. 19.
    Algur, S., Bhat, P.: Web video mining: metadata predictive analysis using classification techniques. Indian J. Inf. Technol. Comput. Sci. (2016)Google Scholar
  20. 20.
    Kunwar, V., Chandal, K., Sabitha, A., Bansal A.: Chronic kidney disease analysis using data mining classification technique. IEEE (2016)Google Scholar
  21. 21.
    UC Irvine Machine Learning Repository.

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.School of Computer ApplicationLPUPhagwaraIndia

Personalised recommendations