Abstract
These days a lot of raw data is generated from various common sources. This large amount of data, which would appear useless at first glance, is very important for companies and researchers as could provide a lot of helpful information. The data could be mined to get useful knowledge that could be used to make fruitful decisions. A lot of online tools and proprietary toolkits are available to the users and it becomes all the more cumbersome for them to know which is the best tool among these for the supervised learning algorithm and datasets they are applying. In order to aid this process, the paper progresses in this direction by doing a comparison of various data mining tools on the basis of their classification finesse. The various tools used in the paper are weka, knime and tanagra. Rigorous work on this has given the result that the performance of the tools is affected by the kind of datasets used and the way in which the supervised learning is done.
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Goyal, A., Khandelwal, I., Anand, R., Srivastava, A., Swarnalatha, P. (2018). A Comparative Analysis of the Different Data Mining Tools by Using Supervised Learning Algorithms. In: Abraham, A., Cherukuri, A., Madureira, A., Muda, A. (eds) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). SoCPaR 2016. Advances in Intelligent Systems and Computing, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-319-60618-7_11
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DOI: https://doi.org/10.1007/978-3-319-60618-7_11
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