Call for Paper - August 2020 Edition
IJCA solicits original research papers for the August 2020 Edition. Last date of manuscript submission is July 20, 2020. Read More

Distributed Linear Programming for Weblog Data using Mining Techniques in Distributed Environment

Print
PDF
International Journal of Computer Applications
© 2010 by IJCA Journal
Number 7 - Article 3
Year of Publication: 2010
Authors:
K. Suresh
Dr Sujni Paul
10.5120/1596-2145

K Suresh and Dr Sujni Paul. Article:Distributed Linear Programming for Weblog Data using Mining Techniques in Distributed Environment. International Journal of Computer Applications 11(7):13–18, December 2010. Published By Foundation of Computer Science. BibTeX

@article{key:article,
	author = {K. Suresh and Dr Sujni Paul},
	title = {Article:Distributed Linear Programming for Weblog Data using Mining Techniques in Distributed Environment},
	journal = {International Journal of Computer Applications},
	year = {2010},
	volume = {11},
	number = {7},
	pages = {13--18},
	month = {December},
	note = {Published By Foundation of Computer Science}
}

Abstract

Distributed learning discusses different strategies in which learners can communicate with each other. The different strategies are data analysis, predicting future learner in an efficient way to access the learning methods. In this paper the distributed learning has proposed an optimized solution for the fore coming learners. The idea of distributed learning is to analyse the weblog data traversed by the previous learners. Data mining is a process of mining the previously unknown data to make shape up useful knowledge or patterns from large databases. The distributed linear programming is the mathematical approach used in this paper to classify web sites from the weblog data for a specific purpose. Distributed learning approach is used in support vector machine and linear regression method. This paper recommends the fore coming learners to go through the identified web links in order to get high score.

Reference

  • Botia J.A., Garijo, J.R., and Skarmeta.A.f.1998. A Generic Data Mining System:Basic Design And Implementation Guidelines in workshop on Distributed Data mining at the Fourth Intl. Conf. on Data Mining and Knowledge Discovery.
  • S. Krishnaswamy, S.W. Loke, A. Zaslavsky. Cost Models for Distributed DataMining school of computer science and software engineering , Monash University.
  • Ning Chen1, Nuno C. Marques1, and Narasimha Bolloju2. AWeb Service-based approach for data mining in distributed environments.
  • Alex J. Smola and Bernhard Scholkopf .(September 30, 2003). A Tutorial on Support Vector Regression.
  • Cherkassky and F. Mulier. 1998. Learning from data. john wiley and sons, New York.
  • Cesar vialardi, Javier Bravo, Leila shafti, Alvaro. Ortigosa. 2009. Recommendation In higher education using data mining techniques.
  • Agrawal. R., Imielinski, T., and Swami, A. 1993. Mining association rules between sets of items in large database. ACM SIGMOD Conference on Management of data.
  • Liu M., Wang F.Y., Zeng D., and YangL. 2001. An Overview of world Wide Caching, IEEE International Conference on Systems, Man and Cybematics.
  • Rousskov, A., and Soloviev, V. 1998. On Performance of Caching Proxies, Short version appears as poster paper in ACM SIGMETRIC’98 Conference.
  • Liu M., Wang F.Y., Zeng D., and YangL. 2001. An Overview of world Wide Caching, IEEE International Conference on Systems, Man and Cybematics, pp. 3045-3050.
  • M. Holsheimer and A. Siebes .1994. The search for knowledge in databases. Technical Report CS-R9406, CWI, Netherlands.
  • R. Kosala and H. Blockeel. June 2000. Web mining research: A survey. SIGKDD Explorations.
  • B. Masand and M. Spiliopoulou, editors. Advances in Web Usage Mining and User Profiling:Proceedings of the WEBKDD’99 Workshop. Number 1836 in LNAI. Springer Verlag,
  • O. Zaiane, M. Xin, and J. Han. April 1998. Discovering Web access patterns and trends by applying OLAP and data mining technology on Web logs. In Proc. Advances in Digital Libraries Conf. (ADL’98), Melbourne, Australia, pages 1244-158.
  • H. Mannila and C. Meek. Aug 2000. Global partial orders from sequential data. In Proc. of the 6th Intl. Conf. on Knowledge Discovery and Data Mining (KDD2000), pages 161–168.
  • R.C Agarwal, C.C Aggarwal and V.V.V. Prasad. A. 2001 tree projection algorithm for generation of frequent item sets. Journal of parallel and distribute computing, 61(3):350-371.
  • S. Parthasarathy, M. Zaki, and W. Li. August 1998 Memory Placement Techniques for Parallel Association mining in the fourth ACM SIGKDD International Conference on knowledge Discovery on knowledge.
  • Josenildo C. da Silvaa, Chris Giannellab,, RuchitaBhargavac, HillolKarguptab,d, Matthias Kluscha “Distributed data mining and agents”, 2005
  • Kun-Ming Yu a, Jiayi Zhou b, Tzung-Pei Hong c, Jia-Ling Zhou d A load-balanced distributed parallel mining algorithm, 2009
  • David meyer, Technishe university at wien, Austria support vector machines Interface to libsvm in package e1071, april 21 2010
  • C . Cortes and V. Vapnik support vector networks. Machine learning, 1995
  • E. Osuna, R. Freund, and F. Girosi. An improved training algorithm for support vector machines. In J. Principe, L. Gile, N. Morgan, and E. Wilson, editors, Neural Networks for Signal Processing VII — Proceedings of the 1997 IEEE Workshop, pages 276 – 285, New York, 1997. IEEE
  • A. J. Smola. Regression estimation with support vector learning machines Master’s thesis, Technische Universit¨at M¨unchen, 1996
  • M. O. Stitson and J. A. E. Weston. Implementational issues of support vector machines Technical Report CSD-TR-96-18, Computational Intelligence Group, Royal Holloway, University of London, 1996
  • V. Vapnik, S. Golowich, and A. Smola. Support vector method for function approximation, regression estimation, and signal processing. In M. Mozer, M. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, pages 281– 287, Cambridge, MA, 1997. MIT Press.
  • S. S. Keerthi and C.-J. Lin. Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation, 15(7):1667{1689, 2003.
  • Pearson K. Mathematical contributions to the theory of evolution. III. Regression, heredity and panmixia. Phil Trans R SocLond Series A 1896; 187:253–318.