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Theoretical Approach of Search of Missing Values in Data Set using Data Mining

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 98 - Number 1
Year of Publication: 2014
Authors:
Ajay Singh Mavai
Sadhna K. Mishra
10.5120/17151-7195

Ajay Singh Mavai and Sadhna K Mishra. Article: Theoretical Approach of Search of Missing Values in Data Set using Data Mining. International Journal of Computer Applications 98(1):41-46, July 2014. Full text available. BibTeX

@article{key:article,
	author = {Ajay Singh Mavai and Sadhna K. Mishra},
	title = {Article: Theoretical Approach of Search of Missing Values in Data Set using Data Mining},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {98},
	number = {1},
	pages = {41-46},
	month = {July},
	note = {Full text available}
}

Abstract

The uncontrollable expansion of the information over the internet creates a critical job to discover which information is supportive or useful for a particular user. This paper proposes a new filtering technique using rough set and clustering technique to seek out the nearest neighbor, So that the user can get the right choice for the collection of the objects which are appropriate for them. In this paper, we are going to find out missing values in data set by using data mining specifically with the help of filtering technique that uses a membership function which is a unique suggesting approach by combining the marking, trait, likes and features of user's information about items. Our approach moves towards the state of the art suggesting system, and reduce the recorded problems. Filtering technique suggest items by taking in to order of the taste of users, under the supposition that users will be attracted by a particular item that users alike to them have rated highly. To our best information, this is the unique study of integrating traits and likes of user's information converted into a missing value for the development of suggesting manager. .

References

  • Bowman, Adedoyin-Olowe, M. , Gaber, M. , Stahl, F. : A Methodology for Temporal Analysis of Evolving Concepts in Twitter. In: Proceedings of the 2013 ICAISC, International Conference on Artificial Intelligence and Soft Computing. 2013.
  • Andreas M. Kaplan, Michael Haenlein: Users of the world, unite! The challenges and opportunities of SM, Business Horizons, Volume 53, Issue 1, January–February 2010, Pages 59-68, ISSN 0007-6813, http://dx. doi. org/10. 1016/j. bushor. 2009. 09. 003.
  • Aggarwal, N. , Liu, H. : Blogosphere: Research Issues, Tools, Applications. ACM SIGKDD Explorations. Vol. 10, issue 1, 20, 2008.
  • Dong, W. 2006. "Influence Modeling of Complex Stochastic Processes. " July. Master's Thesis in Media Arts and Sciences.
  • V. A. Balasubramaniyan, A. Maheswaran, V. Mahalingam, M. Ahamad, and H. Venkateswaran. A crow or a blackbird?: Using true social network and tweeting behavior to detect malicious entities in Twitter. 2010.
  • A. L. Barabasi. The origin of bursts and heavy tails in human dynamics. Nature, 435(7039):207{211, 2005.
  • C. M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
  • J. Ben Schafer, Dan Frankowski, Jon Herlocker, Shilad Sen. Collaborative Filtering Recommender System. 2007, 4321:291-324.
  • Funakoshi , Kaname. A Content Based Collaborative Recommender system with Detailed Use of Evaluations, 2000, 1:253-256.
  • Yao Y Y. Information tables with neighborhood semantics. In: Data Mining and Knowledge Discovery-Theory, Tools, and Technology (Dasarathy B V. Ed. ), Society for Optical Engineering, Bellingham, Washington, 2000, 2: 108~116.