Compressive Study on Various Classification Techniques Used in Data Mining

Authors

  • Charan Singh Tejavath  Research Scholar of Sri Satya Sai University of Technology and Medical Sciences. Sehore, Madhya Pradesh , India
  • Dr. R. P. Singh  Research Supervisor for Sri Satya Sai University of Technology and Medical Sciences Sehore, Madhya Pradesh, India

Keywords:

Classification Technique, Decision Tree Induction, K Nearest Neighbor Classifier, Bayesian Network, and Rule Based Classification. C4.5, ID3, ANN, Naive Bayes, SVM

Abstract

Data Mining is a developing field which has pulled in an expansive number of data enterprises because of the colossal volume of data oversaw as of late. Productive data mining requires a decent comprehension of the data mining techniques to enhance business opportunity and to enhance the nature of administration gave. In light of such needs, this paper gives a survey of conventional classification techniques utilized for data mining. Classification is utilized to discover in which assemble every datum occasion is identified with a given dataset. It is utilized for ordering data into various classes as indicated by a few requirements. A few noteworthy sorts of classification calculations including C4.5, ID3, k-closest neighbor classifier, Naive Bayes, SVM, and ANN are utilized for classification. By and large, a classification system takes three methodologies Statistical, Machine Learning and Neural Network for classification. While considering these methodologies this paper gives a comprehensive review of various classification calculations and their highlights and confinements

References

  1. Nike, Orient. J. Comp. Sci. & Technol., Vol. 8(1), 13-19 (2015), ORIENT JOURNAL COMPUTER SCIENCE AND TECHNOLOGY
  2. J. Han and M. Camber, "Data Mining Concepts and Techniques", Elsevier, 2011.
  3. V. Vapnik and C. Cortes, "Support Vector Network," Machine Learning, 20; 273-297, (1995).
  4. C. J. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, 2; (1998).
  5. H. Bhavsar, A. Ganatra, "A Comparative Study of Training Algorithms for Supervised Machine Learning", International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231 -2307, 2(4); (2012)
  6. G. Wang, "A Survey on Training Algorithms for Support Vector Machine Classifiers", Fourth International Conference on Networked Computing and Advanced Information Management, 2008, IEEE.
  7. G Madzarov, D. Gjorgievikj and I. Chorbev, " A Multi-class SVM Classifier Utilizing Binary Decision Tree", Informatica, pp. 233-241 (2009).
  8. M. Aly, "Survey on Multiclass Classification Methods", November (2005).
  9. V. Vapnik, "Statistical Learning Theory", Wiley, New York, (1998).
  10. T.Joachims, "Making large-scale support vector machine learning practical", In Advances in Kernel Methods: Support Vector Machines, (1999).
  11. J.Platt, "Fast training of SVMs using sequential minimal optimization", In B. Sch¨olkopf, C.Burges and A.Smola (ed.), Advances in Kernel Methods: Support Vector Learning, MIT Press, Cambridge, MA, 1999, 185-208.
  12. D. Michie, D.J. Spiegelhalter, C.C. Taylor "Machine Learning, Neural and Statistical Classification", February 17, (1994).
  13. Delveen Luqman Abd Al.Nabi, Shereen Shukri Ahmed, "Survey on Classification Algorithms for Data Mining: (Comparison and Evaluation)" (ISSN 2222-2863)4(8); (2013)
  14. Riaan Smit" An Overview of Support Vector Machines, 30 March 2011.
  15. Nikita Jain, Vishal Srivastava, "DATA MINING TECHNIQUES: A SURVEY PAPER"-IJRET - Nov 2013
  16. Qiang Yang and Xindongwu, "10 CHALLENGING PROBLEMS IN DATA MINING RESEARCH", International Journal of Information Technology & Decision MakingVol. 5, No. 4 (2006) 597-604
  17. R. Rojas: "The Backpropagation Algorithm", Neural Networks, Springer-Verlag, Berlin, 1996
  18. Chih-Wei Hsu; Chih-Jen Lin, "A comparison of methods for multiclass support vector machines", Neural Networks, IEEE Transactions on (Volume: 13, Issue: 2 ) on Mar 2002
  19. Bing Liu Wynne Hsu Yiming Ma; "Integrating Classification and Association Rule Mining", American Association for Artificial Intelligence -1998
  20. Pernkopf, F.; Wohlmayr, M.; Tschiatschek, S.; "Maximum Margin Bayesian Network Classifiers"; Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume: 34, Issue: 3 )March 2012 
  21. [20]Barros, R.C.; de Carvalho, A.C.P.L.F.; Basgalupp, M.R.; Quiles, M.G.;"A clustering-based decision tree induction algorithm", Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on Nov 2011
  22. P. Domingos and G. Hulten, "Mining high-speed data streams", Proc. 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71-80, 2000.
  23. V. Garcia, E. Debreuve, and M. Barlaud, "Fast k nearest neighbor search using GPU," in Proc. of 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Anchorage, Alaska, USA, June 23-28, 2008, pp. 1-6
  24. Masud, M.M., Jing Gao; Khan, L.; Jiawei Han; Thuraisingham, Bhavani, "A Practical Approach to Classify Evolving Data Streams: Training with Limited Amount of Labeled Data ", Data Mining, 2008. ICDM '08. Eighth IEEE International Conference on 15-19 Dec. 2008
  25. A. Bifet and R. Kirkby, Data Stream Mining a Practical Approach, University of WAIKATO, Technical Report, 2009.
  26. James Dougherty, Ron Kohavi, and MehranSahami.Supervised and unsupervised discretization of continuous features. In International Conference on Machine Learning, pages 194-202, 1995.
  27. David Hand, HeikkiMannila and Padhraic Smyth, Principles of Data Mining, MIT Press, 2001 
  28. Their Nu Phyu, Survey of Classification Techniques in DataMining, Proceedings of the International MultiConference of Engineers and Computer Scientists 2009
  29. E.W.T. Ngai a,*, Li Xiu b, D.C.K. Chau a, Application of data mining techniques in customer relationship management: A literature review and classification, Expert Systems with Applications (2009)
  30. R. Agrawal, T. Imielinski, and A. Swami. Database mining: A performance perspective. IEEE Trans. on Knowledge and Data Engineering, 5(6), Dec. 1993.
  31. E. Manolakos and I. Stamoulis, "IP-cores design for the kNN classifier," in Proc. of IEEE International Symposium on Circuits and Systems, Paris, France, May 30- June 2, 2010, pp. 4133 - 4136.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2018-04-30

Issue

Volume 2, Issue 6, November-December-2017

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Charan Singh Tejavath, Dr. R. P. Singh, " Compressive Study on Various Classification Techniques Used in Data Mining" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.1396-1405, November-December-2017.