Experimental Evaluation of Open Source Data Mining Tools (WEKA and Orange)

Experimental Evaluation of Open Source Data Mining Tools (WEKA and Orange)

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2020 by IJETT Journal
Volume-68 Issue-8
Year of Publication : 2020
Authors : Ritu Ratra , Preeti Gulia
DOI :  10.14445/22315381/IJETT-V68I8P206S

Citation 

MLA Style: Ritu Ratra , Preeti Gulia  "Experimental Evaluation of Open Source Data Mining Tools (WEKA and Orange)" International Journal of Engineering Trends and Technology 68.8(2020):30-35. 

APA Style:Ritu Ratra , Preeti Gulia. Experimental Evaluation of Open Source Data Mining Tools (WEKA and Orange)  International Journal of Engineering Trends and Technology, 68(8),30-35.

Abstract
Nowadays, it is possible for every organisation to manage the large dataset at minimum cost. But in order to collect the fruitful information, it is mandatory to utilize the large volume of stored data. Data mining is an on-going process of searching pattern and collecting useful information from large datasets for future use. There is no doubt that Data mining is very important in various areas like education, military, e-business, healthcare etc. The main objective of data mining process is to supervise the data from various sources in different manner then assemble it to collect the useful information. It can be done by the help of various tools and techniques. There are a number of data mining tools available in the digital world that can help the researchers for the evaluation of the data. These tools work as an interface to receive the data and to extract some meaningful patterns out of large dataset. Selection of best tool according to requirement is not an easy task. In order to find out the best data mining tool for classification problem, comparison of various tools is necessary on the basis of different parameters. In this paper, data mining tools WEKA and Orange are analysed on the basis of implementation of parameters. The main objective of this comparison is to help the researchers to select the suitable tool from these two.

Reference

[1] Jovi?, A., Brki?, K., & Bogunovi?, N. (2014). “An overview of free software tools for general data mining”. Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014 37th International Convention, (May), 26–30. Retrieved from http://www.zemris.fer.hr/~ajovic/articles/MIPRO 2014_final.pdf
[2] Alcalá-Fdez, J., Sánchez, L., & García, S. (2009). “KEEL: a software tool to assess evolutionary algorithms for data mining problems”. Soft Computing. Retrieved from http://link.springer.com/article/10.1007/s00500-008-0323- y
[3] Collier, K., Ph, D., Carey, B., & Marjaniemi, C. (1999). “A Methodology for Evaluating and Selecting Data Mining Software” Keywords : Data Mining , Tool Evaluation , Knowledge Discovery, 00(c), 1–11.
[4] Sonnenburg, S., Braun, M., & Ong, C. (2007). “The need for open source software in machine learning”, 8, 2443– 2466. Retrieved from http://researchcommons.waikato.ac.nz/handle/10289/3928
[5] Chen, X., Ye, Y., Williams, G., & Xu, X. (2007). “A survey of open source data mining systems”. Emerging Technologies in Knowledge Discovery and Data Mining, (60603066), 3– 14. Retrieved from http://link.springer.com/chapter/10.1007/978-3-540- 770183_2
[6] Jovi?, A., Brki?, K., & Bogunovi?, N. (2014). “An overview of free software tools for general data mining”. Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014 37th International Convention, (May), 26–30. Retrieved from http://www.zemris.fer.hr/~ajovic/articles/MIPRO 2014_final.pdf
[7] Kalpana Rangra, Dr. K. L. Bansal. “Comparative Study of Data Mining Tools”, presented at International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 6, 2014.
[8] Dr. Anil Sharma, Balrajpreet Kaur,” A RESEARCH REVIEW ON COMPARATIVE ANALYSIS OF DATA MINING TOOLS, TECHNIQUES AND PARAMETERS”, ISSN No. 0976-5697, International Journal of Advanced Research in Computer Science, volume 8, No. 7, July – August 2017.
[9] H.Witten, E. Frank, M. A.Hall, “Data Mining practiced machine learning tools and techniques”, 3rd ed., Morgan Kaufmann Elsevier: USA,2011.
[10] Predictive Analytics [Online].Available from:http://www.predictiveanalyticstoday.com/topsoftwarefor- text-analysis-text-mining-text-analytics/
[11] Jovi?, A., Brki?, K., & Bogunovi?, N. “An overview of free software tools for general data mining. Information and Communication Technology”, Electronics and Microelectronics (MIPRO), 2014 37th International Convention, (May), 26–30. Retrieved from http://www.zemris.fer.hr/~ajovic/articles/MIPRO 2014_final.pdf
[12] http://www.kdnuggets.com/2015/12/ top-7-newfeaturesorange- 3.html/2
[13] Orange Data Mining, ‘Orange Data Mining Library Documentation Release 3’.
[14] http://orange.biolab.si/
[15] http://Precision%20and%20recall%20-%20Wikipedia.PDF
[16] M.Hall, E.Frank , G.Holmes, B.Reutemann , IH Witten,"The WEKA Data Mining Software: An Update," SIGKDD Explorations,2009.
[17] A.Wahbeh.,"A Comparison Study between Data Mining Tools over some Classification Methods," International Journal of Artificial Intelligence,2012.
[18] Swasti Singhal, Monika Jena. “A Study on WEKA Tool for Data Preprocessing, Classification and Clustering” presented at International Journal of Innovative Technology and Exploring Engineering (IJITEE), Volume-2, Issue-6,2013.
[19] http://www.ionos.com>digitalguide
[20] http://www.google.com
[21] Venkateswarlu Pynam , R Roje Spanadna, Kolli Srikanth, “An Extensive Study of Data Analysis Tools (Rapid Miner, Weka, R Tool, Knime, Orange)”, IJETT International Journal of Computer Science and Engineering ( IJETT – IJCSE ) – Volume 5 Issue 9 – September 2018, ISSN: 2348 – 8387,pp 4-11.
[22] http://opensourceforu.com/2017/03/top-10-open-sourcedatamining- t ools/
[23] Nurdatillah Hasim, Norhaidah Abu Haris, “A Study of Open-Source Data Mining Tools for Forecasting”, IMCOM `15, January 08 - 10 2015, BALI, Indonesia.
[24] Witten, I. H., & Eibe, F. (2005), “Data Mining: Practical Machine Learning Tools and Techniques”, (2nd ed., p. 525).
[25] Sonnenburg, S., Braun, M., & Ong, C., “The need for open source software in machine learning”, 8, 2443–2466. 2007. Retrieved from http://researchcommons.waikato.ac.nz/handle/10289/3928.
[26] 12 data mining tools and techniques [Online]. Available: https://www.invensis.net/blog/data-processing/12- datamining-tools-techniques.
[27] A. kumar, et al., “ Data mining: various issues and challenges for future," IJETA,2014
[28] H. Nasereddin," NEW TECHNIQUE TO DEAL WITH DYNAMIC DATA MINING IN THE DATABASE," IJRRAS,.December 2012.
[29] J.Demšar and B.Zupan, “Orange: Data Mining Fruitful and Fun - A Historical Perspective”, 2012.
[30] C.Shah, A.Jivani, ”Comparison of data mining classification algorithms for breast cancer prediction”, 4th ICCCNT ,IEEE,2013.
[31] P.Kakkar, A.Parashar, “Comparison of different clustering Algorithm using WEKA tool”, International Journal of Advanced Research in Technology, Engineering and Science, 2014.
[32] N.Chauhan and N.Gautam, “Parametric comparison of data mining tools,” IJATES, 2015.
[33] A.Gupta, N.Chetty , S.Shukla, “A classification method to classify High Dimensional data”,IEEE,2015.

Keywords
Classification, Naïve Bayes, Random Forest tree, WEKA, Orange, Precision, Recall.