A Comparative Study of Data Mining Classification Techniques using Lung Cancer Data

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2015 by IJCTT Journal
Volume-22 Number-2
Year of Publication : 2015
Authors : Er.Tapas Ranjan Baitharu, Dr.Subhendu Kumar Pani
  10.14445/22312803/IJCTT-V22P118

MLA

Er.Tapas Ranjan Baitharu, Dr.Subhendu Kumar Pani "A Comparative Study of Data Mining Classification Techniques using Lung Cancer Data". International Journal of Computer Trends and Technology (IJCTT) V22(2):91-95, April 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Now-a-days the most important cause of death for both men and women is the cancer Lung Cancer is a disease of uncontrolled cell growth in tissues of the lung. Detection of Lung Cancer in its early stage is the key of its cure. Data classification is an important task in KDD (knowledge discovery in databases) process. It has several potential applications. The performance of classifiers is strongly dependent on the data set used for learning. It leads to better performance of the classification models in terms of their predictive or descriptive accuracy, diminishing of computing time needed to build models as they learn faster, and better understanding of the models. In this paper, a comparative analysis of data classification accuracy using lung cancer data in different scenarios is presented. The predictive performances of popular classifiers are compared quantitatively.

References
1. Klosgen W and Zytkow J M (eds.), Handbook of data mining and knowledge discovery, OUP, Oxford, 2002.
2. Provost, F., & Fawcett, T., Robust Classification for Imprecise Environments. Machine Learning, Vol. 42, No.3, pp.203-231, 2001.
3. Larose D T, Discovering knowledge in data: an introduction to data mining, John Wiley, New York, 2005.
4. Kantardzic M, Data mining: concepts, models, methods, and algorithms, John Wiley, New Jersey, 2003.
5. Goldschmidt P S, Compliance monitoring for anomaly detection, Patent no. US 6983266 B1, issue date January 3, 2006, Available at: www.freepatentsonline.com/6983266.html
6. Bace R, Intrusion Detection, Macmillan Technical Publishing, 2000.
7. Smyth P, Breaking out of the BlackBox: research challenges in data mining, Paper presented at the Sixth Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD2001), held on May 20 (2001), Santra Barbara, California, USA.
8. Agrawal R. and Srikant R. Fast Algorithms for Mining Association Rules. In M. Jarke J. Bocca and C. Zaniolo, editors, Proceeedings of the 20th International Conference on Very Large Data Bases (VLDB’94), pages 475–486, Santiago de Chile, Chile, Sept 1994 . Morgan Kaufmann.
9. Scheffer T. Finding Association Rules That Trade Support Optimally against Confidence. Unpublished manuscript.
10. Scheffer T. Finding Association Rules That Trade Support Optimally against Confidence. In L. De Raedt and A. Siebes, editors, Proceeedings of the 5th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD’01), pages 424–435, Freiburg, Germany, September 2001. Springer-Verlag.
11. UCI Machine Learning Repository, Available at http://archive.ics.uci.edu/ml/machine-learningdatabases/ statlog/german/.
12. SAS Institute Inc., Lie detector software: SAS Text Miner (product announcement), Information Age Magazine, [London, UK], February 10 (2002), Available at: http://www.sas.com/solutions/fraud/index.html.
13. Berry M J A and Linoff G S, Data mining techniques: for marketing, sales, and relationship management, 2 nd edn (John Wiley; New York), 2004.
14. Delmater R and Hancock M, Data mining explained: a manager's guide to customercentric business intelligence, (Digital Press, Boston), 2002.
15. Fuchs G, Data Mining: if only it really were about Beer and Diapers, Information Management Online, July 1, (2004), Available at: http://www.informationmanagement. com/ news/10061331. html.
16. Subhendu Kumar Pani and Satya Ranjan Biswal and Santosh Kumar Swain, A Data Mining Approach to Identify Key Factors for Systematic Reuse (October 31, 2012). The IUP Journal of Information Technology, Vol. VIII, No. 2, June 2012, pp. 24-34. Available at SSRN: http://ssrn.com/abstract=2169262
17. Subhendu Kumar Pani and Amit Kumar and Maya Nayak,‖ Performance Analysis of Data Classification Using Feature Selection‖ (October 24, 2013). The IUP Journal of Information Technology, Vol. IX, No. 2, June 2013, pp. 36-50.

Keywords
Classification, Data Mining, Lung Cancer, Naive Bayes.