Analytical Research on Decision Tree Algorithm and Naive Bayesian Classification Algorithm for Data Mining

Authors(3) :-Mohammed Mustaqim Rahman, Tarikuzzaman Emon, Zonayed Ahmed

The paper presents an extensive modification of ID3 (Iterative Dichotomiser) algorithm and Naive Bayesian Classification algorithm for data mining. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Most companies already collect and refine massive quantities of data. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and systems as they are brought on-line. This paper provides an introduction to the basic technologies of data mining. Examples of profitable applications illustrate its relevance to today's business environment as well as a basic description of how data warehouse architectures can evolve to deliver the value of data mining to end users.

Authors and Affiliations

Mohammed Mustaqim Rahman
Departement Informatique, Universite de Lorraine, Nancy, France
Tarikuzzaman Emon
Department of Computer Science and Engineering, Stamford University Bangladesh, Dhaka, Bangladesh
Zonayed Ahmed
Department of Computer Science and Engineering, Stamford University Bangladesh, Dhaka, Bangladesh

ID3, Naive Bayesian, Algorithm, Data mining, Database.

  1. Decision Tree Algorithms: Integration of Domain Knowledge for Data Mining, Aukse Stravinskiene, Saulius Gudas, and Aiste Davrilaite; 2012
  2. S.B. Kotsiantis, Supervised Machine Learning: A Review of Classification Techniques, Informatica 31(2007) 249-268, 2007
  3. K. Karimi and H.J. Hamilton, Logical Decision Rules: Teaching C4.5 to Speak Prolog, IDEAL, 2000
  4. Rennie, J.; Shih, L.; Teevan, J.; Karger, D. (2003). "Tackling the poor assumptions of Naive Bayes classifiers"
  5. John, George H.; Langley, Pat (1995). "Estimating Continuous Distributions in Bayesian Classifiers". Proc. Eleventh Conf. on Uncertainty in Artificial Intelligence. Morgan Kaufmann. pp. 338–345
  6. D. Pregibon, "Data Mining", Statistical Computing and Graphics,vol. 7, no. 3, p. 8, 1996.
  7. U. Fayyad, Advances in knowledge discovery and data mining. Menlo Park, Calif.: AAAI, 1996.
  8. R. Kimball, "The Data Webhouse Toolkit: Building the Web‐enabled Data Warehouse20001 The Data Webhouse Toolkit: Building the Web‐enabled Data Warehouse. John Wiley & Son,, ISBN: 0‐471‐37680‐9 £32.50 Paperback", Industr Mngmnt & Data Systems, vol. 100, no. 8, pp. 406-408, 2000.
  9. M. Betts, "The Almanac:Hot Tech", Computerworld, 2003. [Online]. Available: http://www.computerworld.com/article/2574084/data-center/the-almanac.html

Publication Details

Published in : Volume 2 | Issue 2 | March-April 2016
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 755-766
Manuscript Number : IJSRSET1622190
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Mohammed Mustaqim Rahman, Tarikuzzaman Emon, Zonayed Ahmed, " Analytical Research on Decision Tree Algorithm and Naive Bayesian Classification Algorithm for Data Mining, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 2, pp.755-766, March-April-2016.
Journal URL : http://ijsrset.com/IJSRSET1622190

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