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Reseach Article

An Efficient Way for Data Mining via Overlay-based Networking for Enhanced Service

by Pushpanjali, Jyothi S. Nayak
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 123 - Number 16
Year of Publication: 2015
Authors: Pushpanjali, Jyothi S. Nayak
10.5120/ijca2015905751

Pushpanjali, Jyothi S. Nayak . An Efficient Way for Data Mining via Overlay-based Networking for Enhanced Service. International Journal of Computer Applications. 123, 16 ( August 2015), 24-30. DOI=10.5120/ijca2015905751

@article{ 10.5120/ijca2015905751,
author = { Pushpanjali, Jyothi S. Nayak },
title = { An Efficient Way for Data Mining via Overlay-based Networking for Enhanced Service },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 123 },
number = { 16 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 24-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume123/number16/22045-2015905751/ },
doi = { 10.5120/ijca2015905751 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:12:53.750479+05:30
%A Pushpanjali
%A Jyothi S. Nayak
%T An Efficient Way for Data Mining via Overlay-based Networking for Enhanced Service
%J International Journal of Computer Applications
%@ 0975-8887
%V 123
%N 16
%P 24-30
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Big data generated from various aspects like online transactions, social websites, logs and search queries is increasing rapidly and thus the demand for data mining has risen as a noteworthy zone. An overlay- based parallel information mining executes completely dispersed information administration and handles processing by utilizing the overlay system, which can achieve high flexibility. The talk incorporates a survey of best in class systems and stages for preparing and overseeing huge information and also the endeavours expected on enormous information mining. Nonetheless, the overlay - based parallel mining structural planning is not fit for achieving data mining administrations if there is an occurrence of the physical system disturbance that is created due to switch/correspondence line breakdowns on the grounds that various hubs are expelled from the overlay system. To get the estimated arrangement and better results, the proposed framework utilizes K-medoids algorithm for cluster formation and overlay based system. Proposed work gives enhancement in terms Energy Consumption in data gathering, reduced delay and Node Coverage.

References
  1. Data Mining with Big Data, vol. 26, no. 1, IEEE, 2014.
  2. Ubiquitous Analytics: Interacting with Big Data Anywhere, Anytime, vol.46, Issue 4, IEEE, 2013.
  3. http://documents.software.dell.com/statistics/textbook/data-mining-techniques
  4. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., et al. (2000), CRISP-DM 1.0, Chikago, IL. SPSS.
  5. Jiawei Han and MichelineKamber (2006), “Data Mining Concepts and Techniques”, published by Morgan Kauffman, 2nd ed.
  6. Mrs. Bharati M. Ramageri, Lecturer, “DATA MINING TECHNIQUES AND APPLICATIONS”, Indian Journal of Computer Science and Engineering, Vol. 1 No. 4 301-305.
  7. Data Mining at a Glance @Source:http://publib.boulder.ibm.com/infocenter/db2luw/v9r5/index.jsp?topic=%2Fcom.ibm.im.easy.doc%2Fc_dm_process.html
  8. Mohammad HosseinAnisi, Abdul Hanan Abdullah, ShukorAbdRazak, “Energy-Efficient DataCollection in Wireless Sensor Networks”, Wireless Sensor Network, pp.329-333, October 2011.
  9. Xin Guan, Lin Guan and Xingang Wang, “A Novel Energy Efficient Clustering Technique Based on Virtual Hexagon for Wireless Sensor Networks”, Volume 7, Issn 1349-4198, pp. 1891-1904, April,2011.
  10. Shio Kumar Singh, M P Singh, and D K Singh, “Energy Efficient Homogenous Clustering Algorithm for Wireless Sensor Networks”, International Journal of Wireless & Mobile Networks (Ijwmn), Vol.2, No.3, August 2010.
  11. D. Kumar, T.C. Aseri, R.B. Pate, “Energy Efficient Clustering and Data Aggregation Protocol for Heterogeneous Wireless Sensor Networks”, ISSN 1841-9836, E-Issn 1841-9844 Vol. No. 1, pp. 113-124, (March 2011).
  12. M. Ahmed and S. Vorobyov, “Collaborative Beamforming for Wireless Sensor Networks with Gaussian Distributed Sensor Nodes,” Wireless Communications, IEEE Transactions, Vol. 8, No. 2,pp. 638 –643, Feb.2009.
  13. KiranMaraiya, Kamal Kant, Nitin Gupta “Architectural Based Data Aggregation Techniques in Wireless Sensor Network: A Comparative Study”, International Journal on Computer Science and Engineering (IJCSE), Vol. 3 No. 3 Mar 2011
  14. An Overlay-Based Data Mining Architecture Tolerant to Physical Network Disruptions, IEEE Std.10.1109, 2014.
  15. Wenjun Xiao, Mingxin He and Huomin Liang, “Cayley CCC: A Robust P2P Overlay Network with Simple Routing and Small-World Features”, Journal of Networks, vol. 6, Issue 9,September 2011
  16. KatsuyaSuto, Hiroki Nishiyama, XueminShen and Nei Kato1, “Designing P2P Networks Tolerant to Attacks and Faults Based on Bimodal Degree Distribution”, Journal of Communications, vol 7, Issue 8, August 2012.
  17. Nikita Jain and Vishal Srivastava “Data Mining Techniques: A Survey Paper”, International Journal of Research in Engineering and Technology, vol. 2, Issue 11, November 2013.
  18. Distributed Data Mining in Peer-to-Peer Networks, IEEE Std.1089-7801,2006.
  19. DunrenChe, MejdlSafran, and Zhiyong Peng, From Big Data to Big Data Mining: Challenges, Issues, and Opportunities,B. Hong et al. (Eds.): DASFAA Workshops 2013,Springer-Verlag Berlin Heidelberg 2013.
  20. Michael Cardosa, Aameek Singh,Himabindu Pucha and Abhishek Chandra,"Exploiting Spatio-Temporal Tradeoffs for Energy-aware MapReduce in the Cloud", IEEE 4th International Conference on Cloud Computing, 2011.
  21. A. P. Reynolds, G. Richards, and V. J. Rayward-Smith, ”The Application of K-medoids and PAM to the Clustering of Rules”
  22. Shalini S Singh, and N C Chauhan, “K-means v/s K-medoids:A Comparative Study”.
Index Terms

Computer Science
Information Sciences

Keywords

Parallel Data Mining Big Data Overlay-based versatility Kmeans K-medoids physical network disruption.