Skip to main content
SpringerLink
Log in

Analyzing Factors Affecting the Performance of Data Mining Tools

  • Conference paper
  • First Online:
Advanced Informatics for Computing Research (ICAICR 2017)

Part of the book series: Communications in Computer and Information Science ((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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Rnageri, B.: Data mining techniques and applications. Indian J. Comput. Sci. Eng. (2010)

    Google Scholar 

  2. Agrawal, H., Agrawal, P.: Review on data mining tools. IJISET 1, 52–56 (2014)

    Google Scholar 

  3. Kumar, R., Verma, R.: Classification algorithm for data mining: a survey. IJIET 1, 7–14 (2012)

    Google Scholar 

  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. Patel, S., Desai, S.: A comparative study on data mining tools. IJATCSE (2015)

    Google Scholar 

  6. Krstevski, J., Mihajlove, D., Chorbev, I.: Student data analysis with rapid miner. In: ICT Innovations (2011)

    Google Scholar 

  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. Hall, M., Frank, E., Holmes, G.: The WEKA data mining software: an update. SIGKDD Explor. (2009)

    Google Scholar 

  9. Demšar, J., Zupan, B.: Orange: data mining fruitful and fun - a historical perspective (2012)

    Google Scholar 

  10. Loper, E., Bird, S.: NLTK: The Natural Language Toolkit (2002)

    Google Scholar 

  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. 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. 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. 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. 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. Patel, J.: Classification algorithm and comparison in data mining. Int. J. Innov. Adv. Comput. Sci. (2015)

    Google Scholar 

  17. Khan, A., Ahmed, S.: Comparative analysis of data mining tools for lung cancer patients. J. Inf. Commun. Technol. (2015)

    Google Scholar 

  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. Algur, S., Bhat, P.: Web video mining: metadata predictive analysis using classification techniques. Indian J. Inf. Technol. Comput. Sci. (2016)

    Google Scholar 

  20. Kunwar, V., Chandal, K., Sabitha, A., Bansal A.: Chronic kidney disease analysis using data mining classification technique. IEEE (2016)

    Google Scholar 

  21. UC Irvine Machine Learning Repository. http://archive.ics.uci.edu/ml/

Download references

Author information

Authors and Affiliations

  1. School of Computer Application, LPU, Phagwara, Punjab, India

    Balrajpreet Kaur & Anil Sharma

Authors
  1. Balrajpreet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anil Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Balrajpreet Kaur .

Editor information

Editors and Affiliations

  1. Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh

  2. IIT Roorkee, Roorkee, India

    Balasubramanian Raman

  3. CTIEMT, Jalandhar, Punjab, India

    Ashish Kumar Luhach

  4. Department of Mathematics and Computing Science, Saint Mary’s University, Halifax, Nova Scotia, Canada

    Pawan Lingras

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Kaur, B., Sharma, A. (2017). Analyzing Factors Affecting the Performance of Data Mining Tools. In: Singh, D., Raman, B., Luhach, A., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2017. Communications in Computer and Information Science, vol 712. Springer, Singapore. https://doi.org/10.1007/978-981-10-5780-9_19

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-5780-9_19

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5779-3

  • Online ISBN: 978-981-10-5780-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation