Experimental Evaluation of Open Source Data Mining Tools: R, Rapid Miner and KNIME
Hemlata1, Preeti Gulia2

1Hemlata*, Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, India.
2Preeti Gulia, Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, India. 

Manuscript received on October 16, 2019. | Revised Manuscript received on 25 October, 2019. | Manuscript published on November 10, 2019. | PP: 4133-4144 | Volume-9 Issue-1, November 2019. | Retrieval Number: A5341119119/2019©BEIESP | DOI: 10.35940/ijitee.A5341.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In the current scenario of Big Data, open source Data Mining tools are very popular in business data analytics. The paper presents a comprehensive study of three most popular open source data mining tools – R, RapidMiner and KNIME. The tools are compared by implementing them on two real datasets. Performance is evaluated by creating a decision tree of the datasets taken. Our objective is to find the best tool for classification. The study can help researchers, developers and users in selecting a tool for accuracy in their data analysis and prediction. Experiments depict that accuracy level of the tool changes with the quantity and quality of the dataset. The results show that RapidMiner is the best tool followed by KNIME and R.
Keywords: Classification, Data Mining tools, Decision tree, KNIME , R, RapidMiner
Scope of the Article: Classification