CFP last date
20 December 2024
Reseach Article

Efficient Data Retrieval using Combine Approach of SOM and K-Mean Clustering

by Deepa Sharma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 147 - Number 9
Year of Publication: 2016
Authors: Deepa Sharma
10.5120/ijca2016911183

Deepa Sharma . Efficient Data Retrieval using Combine Approach of SOM and K-Mean Clustering. International Journal of Computer Applications. 147, 9 ( Aug 2016), 33-38. DOI=10.5120/ijca2016911183

@article{ 10.5120/ijca2016911183,
author = { Deepa Sharma },
title = { Efficient Data Retrieval using Combine Approach of SOM and K-Mean Clustering },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2016 },
volume = { 147 },
number = { 9 },
month = { Aug },
year = { 2016 },
issn = { 0975-8887 },
pages = { 33-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume147/number9/25684-2016911183/ },
doi = { 10.5120/ijca2016911183 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:51:29.599196+05:30
%A Deepa Sharma
%T Efficient Data Retrieval using Combine Approach of SOM and K-Mean Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 147
%N 9
%P 33-38
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Emergence of recent techniques for scientific knowledge collection has resulted in large scale accumulation of information relating various fields. Typical info querying ways are inadequate to extract helpful data from huge knowledge banks. Cluster analysis is one of the key knowledge analysis way and the k-means clustering algorithm is widely used for several data mining applications. The analysis of the cancer data set with the k mean and then applying with the Som. Many ways are planned within the literature for improving the performance with the k-means clustering formula. This paper proposes a technique for creating knowledge retrieval more practical and efficient using som with K mean clustering technique, So as to get better clustering with reduced quality.

References
  1. Jiawei Han M. K, Data Mining Concepts and Techniques, Morgan Kaufmann Publishers, An Imprint of Elsevier, 2006.
  2. Margaret H. Dunham, Data Mining- Introductory and Advanced Concepts, Pearson Education, 2006.
  3. McQueen J, “Some methods for classification and analysis of multivariate observations,” Proc. 5th Berkeley Symp. Math. Statist. Prob., (1):281–297, 1967.
  4. Pang-Ning Tan, Michael Steinback and Vipin Kumar, Introduction to Data Mining, Pearson Education, 2007.
  5. Stuart P. Lloyd, “Least squares quantization in pcm,” IEEE Transactions on Information Theory, 28(2): 129-136.
  6. L. Getoor and C. Diehl, “Link mining: A survey,” SIGKDD Explor. Newslett., vol. 7, pp. 3–12, Dec. 2005.
  7. Q. Lu and L. Getoor, “Link-based Classification,” in Proc. 20th Int. Conf. Machine Learning, Washington, DC, USA, 2003, pp. 496–503.
  8. A. Ng, A. Zheng, and M. Jordan, “Stable algorithms for link analysis,” in Proc. SIGIR Conf. Inform. Retrieval, New Orleans, LA, USA, 2001, pp. 258–266.
  9. B. Taskar, M. Wong, P. Abbeel, and D. Koller, “Link prediction in relational data,” presented at the Adv. Neural Inform. Process. Syst., Vancouver, Canada, 2003.
  10. D. Liben-Nowell and J. M. Kleinberg, “The link prediction problem for social networks,” J. Amer. Soc. Inform. Sci. Technol., vol. 57, pp. 556–559, May 2007.
  11. Z. Lacroix, H. Murthy, F. Naumann, and L. Raschid, “Links and paths through life sciences data sources,” in Proc. 1st Int.Workshop Data Integr. Life Sci., Leipzig, Germany, 2004, pp. 203–211.
  12. J. Noessner, M. Niepert, C. Meilicke, and H. Stuckenschmidt, “Leveraging terminological structure for object reconciliation,” in The Semantic Web: Research and Applications. Berlin, Germany: Springer, 2010, pp. 334–348.
  13. M. E. J. Newman, “Detecting community structure in networks,” Eur. Phys. J., vol. 38, pp. 321–330, Mar. 2004.
  14. J. Huan and J. Prins, “Efficient mining of frequent subgraphs in the presence of isomorphism,” in Proc. 3rd IEEE Int. Conf. Data Mining, Melbourne, FL, USA, 2003, pp. 549–552.
  15. D. Hand, “Principles of data mining,” Drug Safety, vol. 30, pp. 621–622, Jul. 2007.
  16. J. Hans and M. Kamber, Data Mining: Concepts and Techniques, 2nd ed. Burlington, MA, USA: Morgan Kaufmann, 2006.
  17. S. Deelers and S. Auwatanamongkol, “Enhancing K-Means Algorithm with Initial Cluster Centers Derived from Data Partitioning along the Data Axis with the Highest Variance,” International Journal of Computer Science, Vol. 2, Number 4.
  18. Margaret H Dunham, Data Mining-Introductory and Advanced Concepts, Pearson Education, 2006.
  19. I. Mierswa, M. Wurst, W. Michael, R. Klinkenberg, M. Scholz, and T. Euler, “YALE: Rapid prototyping for complex data mining tasks,” in Proc. 12th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, Philadelphia, PA, USA, 2006, pp. 935–940.
  20. P. Bonato, P. J. Mork, D. M. Sherill, and R. H. Westgaard, “Data mining of motor patterns recorded with wearable technology,” IEEE Eng. Med. Biol. Mag., vol. 22, no. 3, pp. 110–119, May/Jun. 2003.
  21. J. Vesanto, J. Himberg, E. Alhoniemi, and J. Parhankangas, “Self- Organizing Map in MATLAB: The SOM Toolbox,” in Proc. Matlab DSP Conf., Espoo, Finland, 1999, pp. 35–40.
  22. Yuan F, Meng Z. H, Zhang H. X and Dong C. R, “A New Algorithm to Get the Initial Centroids,” Proc. of the 3rd International Conference on Machine Learning and Cybernetics, pages 26–29, August 2004.
  23. Fahim A.M, Salem A. M, Torkey A and Ramadan M. A, “An Efficient enhanced k-means clustering algorithm,” Journal of Zhejiang University, 10(7):1626–1633, 2006.
  24. Xiaodong Feng1, Amie Cai, Kevin Dong, Wendy Chaing, Max Feng, Nilesh S Bhutada, John Inciardi, Tibebe Woldemariam Feng, “Assessing Pancreatic Cancer Risk Associated with Dipeptidyl Peptidase 4 Inhibitors: Data Mining of FDA Adverse Event Reporting System (FAERS)”, J Pharmacovigilance 2013, http://dx.doi.org/10.4172/2329-6887.1000110.
  25. Juha Vesanto, Johan Himberg, Esa Alhoniemi and Juha Parhankangas, “Self-organizing map in Matlab: The SOM Toolbox”, Proceedings of the Matlab DSP Conference 1999, Espoo, Finland, November 16–17, pp. 35–40, 1999.
  26. Cai-Hong Yun, Kristen E. Mengwasser, Angela V. Toms, Michele S. Woo, Heidi Greulich, Kwok-Kin Wong, Matthew Meyerson, Michael J. Eck, “The T790M mutation in EGFR kinase causes drug resistance by increasing the affinity for ATP”, Pp. 2070–2075, PNAS, February 12, 2008, vol. 105 no.6,www.pnas.org_cgi_doi_10.1073_pnas.0709662105.
  27. Katherine Faust, Metodoloski Zvezki, “Comparing Social Networks: Size, Density, and Local Structure”, Vol. 3, No. 2, 2006, 185-216.
  28. Altug Akay, Andrei Dragomir, Bjorn Erik Erlandsson, “A Novel Data-Mining Approach Leveraging Social Media to Monitor Consumer Opinion of Sitagliptin”, IEEE Journal Of Biomedical And Health Informatics, Vol. 19, No. 1, Pp. 2168-2194, January 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Clustering Health Care SOM K-Mean False detection.