Skip to main content

A Comparison of Data Mining Tools and Classification Algorithms: Content Producers on the Video Sharing Platform

  • Conference paper
  • First Online:
Book cover Artificial Intelligence and Applied Mathematics in Engineering Problems (ICAIAME 2019)

Abstract

With the development of internet technologies, the use of video sharing sites has increased. Video sharing sites allow users to watch videos of others. In addition, users can create an account to upload content and upload videos. These platforms stand out as the places where individuals are both producers and consumers. In this study, data about YouTube which is a video sharing site was used. The content of the content, which is also called as a channel on YouTube, was made by using a set of producers. The data set with 5000 samples on YouTube channels is taken from Kaggle. The data were classified using 4 different data mining tools such as Weka, RapidMiner, Knime and Orange using Naive Bayes and Random Forest algorithms. The parameters are requested from the user in order to obtain a more efficient result in the application of data mining algorithms and in the data preprocessing steps and in the data mining steps. Although these parameters are common in some data mining software, they are not included in all data mining software. Data mining software provides management of some parameters while other parameters cannot be managed. These changes affect the accuracy value in the study and affect the accuracy value in different ratios. Changing the values of the parameters revealed differences in the accuracy rates obtained. A data mining software model has been proposed by emphasizing to what extent the management of the parameters of the study and the extent of the management of the parameters should be connected to the data mining software developer.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. ALEXA Homepage. https://www.alexa.com. Accessed 15 Dec 2018

  2. OMNICOREAGENCY Homepage. https://www.omnicoreagency.com. Accessed 16 Dec 2018

  3. PRESS Homepage. https://www.youtube.com/yt/about/press. Accessed 16 Dec 2018

  4. Ata, A., Atik, A.: ALTERNATİF BİR EĞİTİM-ÖĞRETİM ORTAMI OLARAK VİDEO PAYLAŞIM SİTELERİ: ÜNİVERSİTELERDEKİ YOUTUBE UYGULAMALARI. Soc. Sci. 11(4), 312–325 (2016)

    Google Scholar 

  5. Yıldırım, N., Özmen, B.: Video paylaşim sitelerinin eğitsel amaçli kullanimi. Educ. Sci. 7(1), 288–295 (2012)

    Google Scholar 

  6. Jovic, A., Brkic, K., Bogunovic, N.: An overview of free software tools for general data mining. In: 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1112–1117. IEEE, Croatia (2014)

    Google Scholar 

  7. Chen, X., Ye, Y., Williams, G., Xu, X.: A survey of open source data mining systems. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 3–14. Springer, Heidelberg (2014)

    Google Scholar 

  8. Wahbeh, A.H., Al-Radaideh, Q.A., Al-Kabi, M.N., Al-Shawakfa, E.M.: A comparison study between data mining tools over some classification methods. Int. J. Adv. Comput. Sci. Appl. 8(2), 18–26 (2011)

    Google Scholar 

  9. King, M.A., Elder, J.F., Gomolka, B., Schmidt, E., Summers, M., Toop, K.: Evaluation of fourteen desktop data mining tools. In: 1998 IEEE International Conference on Systems, Man, and Cybernetics, vol. 3, pp. 2927–2932. IEEE, Beijing (1998)

    Google Scholar 

  10. Sharma, N., Bansal, K.L.: Comparative study of data mining tools. J. Adv. Database Manag. Syst. 2(2), 35–41 (2015)

    Google Scholar 

  11. Dusanka, D., Darko, S., Srdjan, S., Marko, A., Teodora, L.: A comparison of contemporary data mining tools. In: XVII International Scientific Conference on Industrial Systems, IS 2017, pp. 150–155, Novi Sad (2017)

    Google Scholar 

  12. Köktürk, F., Ankarali, H., Sümbüloğlu, V.: Veri Madenciliği Yöntemlerine Genel Bakış. Turkiye Klinikleri J. Biostat. 1(1), 20–25 (2009)

    Google Scholar 

  13. Kittler, R., Wang, W.: The emerging role for data mining. Solid State Technol. 42(11), 45 (1999)

    Google Scholar 

  14. Ozekes, S.: Veri Madenciligi Modelleri ve Uygulama Alanlari. Istanb. Ticaret Univ. J. 3, 65–82 (2003)

    Google Scholar 

  15. Oğuzlar, A.: Veri ön işleme. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 21, 67–76 (2003)

    Google Scholar 

  16. Kaya, M., Özel, S.A.: Açık Kaynak Kodlu Veri Madenciliği Yazılımlarının Karşılaştırılması. In: 14th Academy Information Conference, pp. 47–53, Mersin (2014)

    Google Scholar 

  17. WEKA Homepage. https://www.cs.waikato.ac.nz/ml/weka. Accessed 17 Dec 2018

  18. KNIME Homepage, https://www.knime.com. Accessed 17 Dec 2018

  19. Akpolat, O., Odabaş, S.Ç.: KNIME Yazilimi Ile Kimyasal Analiz Verilerinin Kümelenmesi. In: 16XVI. Akademik Bilişim Konferansı, Aydın (2016)

    Google Scholar 

  20. RAPIDMINER, Homepage. https://rapidminer.com. Accessed 18 Dec 2018

  21. Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hočevar, T., Milutinovič, M., Polajnar, M., Toplak, M., Starič, A., Štajdohar, M.: Orange: data mining toolbox in Python. J. Mach. Learn. Res. 14(1), 2349–2353 (2013)

    MATH  Google Scholar 

  22. Düzdar, İ., TemüR, G.: Comparing of results and implementation of clustering methods of data mining software with a data set. In: Electric Electronics, Computer Science, Biomedical Engineering’s Meeting (EBBT), pp. 1–5. IEEE, Istanbul (2017)

    Google Scholar 

  23. Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9(4), 309–347 (1992)

    MATH  Google Scholar 

  24. Çaliş, K., Gazdaği, O., Yildiz, O.: Reklam İçerikli Epostaların Metin Madenciliği Yöntemleri ile Otomatik Tespiti. Bilişim Teknolojileri Dergisi 6(1), 1–7 (2013)

    Google Scholar 

  25. KAGGLE Homepage. https://www.kaggle.com. Accessed 18 Dec 2018

  26. Yeşilbudak, M., Kahraman, H. T., Karacan, H.: Veri madenciliğinde nesne yönelimli birleştirici hiyerarşik kümeleme modeli. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 26(1), 27–39 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ercan Atagün .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Atagün, E., Argun, İ.D. (2020). A Comparison of Data Mining Tools and Classification Algorithms: Content Producers on the Video Sharing Platform. In: Hemanth, D., Kose, U. (eds) Artificial Intelligence and Applied Mathematics in Engineering Problems. ICAIAME 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 43. Springer, Cham. https://doi.org/10.1007/978-3-030-36178-5_42

Download citation

Publish with us

Policies and ethics