Similarity Based Cluster Analysis on Engineering Materials Data Sets

Part of the Advances in Intelligent Systems and Computing book series (volume 167)

Abstract

Nowadays with rapidly growing databases in manufacturing industries it’s really an unmanageable timing problem to analyze them and to make decision from them. Studying this type of problem using data mining techniques leads more clarification for manufacture and also for better research work. Here in this paper a similarity based cluster technique is proposed on engineering materials database and implemented using c sharp .net.

Keywords

Clustering Engineering materials K-mean 

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References

  1. 1.
    Pham, D.T., Afify, A.: Clustering techniques and their applications in engineering. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 221, 1445–1459 (2007)CrossRefGoogle Scholar
  2. 2.
    Doreswamy, Sharma, S.C.: An Expert Decision Support System for Engineering Materials Selections And Their Performance Classifications on Design Parameters. International Journal of Computing and Applications (ICJA) 1(1), 17–34 (2006)Google Scholar
  3. 3.
    Doreswamy: Similarity measuring approach based engineering materials selection. International Journal of Computational Intelligence Systems (IJCIS) 3, 115–122 (2010)CrossRefGoogle Scholar
  4. 4.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn.Google Scholar
  5. 5.
    Teknomo, K.: K-Means Clustering Tutorials, http://people.revoledu.com/kardi/tutorial/kMean/
  6. 6.
    Grljevic, O., Bošnjak, Z.: Combining different Clustering Techniques for Improved Knowledge Discovery. In: Proceedings of the 20th Central European Conference on Information and Intelligent Systems, pp. 287–292 (September 2009)Google Scholar
  7. 7.
    Santhi, P., Murali Bhaskaran, V.: Performance of Clustering Algorithms in Healthcare Database. International Journal for Advances in Computer Science 2(1), 26–31 (2010) ISSN - 2218-6638Google Scholar
  8. 8.
    Chauhan, R., Kaur, H., Afshar Alam, M.: Data Clustering Method For Discovering Clusters In Spatial Cancer Databases. International Journal of Computer Applications (0975 – 8887) 10(6), 9–14 (2010)CrossRefGoogle Scholar
  9. 9.
    Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An Efficient k-Means Clustering Algorithm: Analysis and Implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7) (July 2002)Google Scholar
  10. 10.
    Kumar, V., Rathee, N.: Knowledge discovery from database Using an integration of clustering and classification. International Journal of Advanced Computer Science and Applications 2(3), 29–33 (2011)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Post-Graduate Studies and Research in Computer ScienceMangalore UniversityMangalagangotriIndia

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