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The Adaptability of Decision Tree Method in Mining Industry Safety Data

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 50 - Number 22
Year of Publication: 2012
Vincent I. Nnebedum

Vincent I Nnebedum. Article: The Adaptability of Decision Tree Method in Mining Industry Safety Data. International Journal of Computer Applications 50(22):4-10, July 2012. Full text available. BibTeX

	author = {Vincent I. Nnebedum},
	title = {Article: The Adaptability of Decision Tree Method in Mining Industry Safety Data},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {50},
	number = {22},
	pages = {4-10},
	month = {July},
	note = {Full text available}


Data Mining nowadays is extensively applied in researches and business fields. The choice of data mining tools is practically dependent on the nature of the data to be mined. Studies have shown that certain tools perform better in certain types of dataset. This paper x-rays a typical real life incident dataset common to oil and gas industries and how decision tree algorithm fits into mining it. The application of Decision Tree mining tool on a real-world safety records perfectly reveals useful information that are subsumed in the volume and nature of the data. The concept of Entropy and Information Gain theory are used in building the decision tree model and an accuracy of 71. 4% arrived at, indicating a good performance of decision tree induction on the dataset. Further areas of research on the use of decision tree method in data mining are recommended.


  • Pang-Ning Tan, Michael Steinbach, Vipin Kumar, 2006, Introduction to Data Mining, Addison-Wesley Companion Books.
  • Abbott D. W, Matkovsky P. I and Elder J. F, 1998, An Evaluation of High-end Data Mining Tools for Fraud Detection, IEEE International Conference on Systems, Man, and Cybernetics, San Diego, CA, October 12-14, 1998. Paper download: http://datamininglab. com/Portals/0/tool%20eval%20articles/smc98_abbott_mat_eld. pdf
  • Kurt Thearling, 2007, White Paper: An Introduction to Data Mining: Discovering hidden value in your data warehouse, [http://www. thearling. com/text/dmwhite/dmwhite. htm]
  • Gentle J. E, Hardle W, Mori Y, 2004, Handbook of Computational Statistics, Springer Edition, ISBN 10-3540404643.
  • Alsabti K, Ranka S, and Singh V, August 1998. CLOUDS: Decision Tree Classification for Large Database. In Proceeding of 4th Intl. Conf. on Knowledge Discovery on Data Mining. New York, NY,
  • Peter Cabena, Pablo Hadjinain, et al, 1998 Discovering Data Mining From Concept to Implementation, Prentice Hall PTR.
  • Edelstein, Herbert A. 1999, Introduction to Data Mining and Knowledge Discovery, Third Edition. Potomac, Two Crows Corporation,.
  • Jiawei Han, Micheline Kamber, 2006, Data Mining: Concepts and Techniques, The Morgan Kaufmann Series in Data Management Systems, ISBN 558609016.
  • Ian H. Witten, Eibe Frank, Mark A. Hall; Data Mining, 2011, Practical Machine Learning Tools and Techniques (3rd Edition) Morgan Kaufmann, ISBN 978-0-12-374856-0
  • Mitchell T, 1997, Decision Tree Learning: Machine Learning, The McGraw-Hill Companies, Inc.
  • J. Ross Quinlan, 1975, Machine Learning, vol. 1, no 1.
  • Hand D. J, Manila H, Smyth P, 2001, Principles of Data Mining, MIT Press
  • Galit S, Nitin R. P, Peter C. B, 2010, Data Mining for Business Intelligence: Concepts, Techniques and Applications in Microsoft Office Excel with XLMiner, 2nd Edition, John Wiley & Sons, Inc. New Jersey, ISBN 978-0-470-52682-8.