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Comparative Study of Techniques and Issues in Data Clustering

  • Parneet KaurEmail author
  • Kamaljit Kaur
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 8)

Abstract

Data mining refers to the extraction of obscured prognostic details of data from large databases. The extracted information is visualized in the form of charts, graph, tables and other graphical forms. Clustering is an unsupervised approach under data mining which groups together data points on the basis of similarity and separate them from dissimilar objects. Many clustering algorithms such as algorithm for mining clusters with arbitrary shapes (CLASP), Density peaks (DP) and k-means are proposed by different researchers in different areas to enhance clustering technique. The limitation addressed by one clustering technique may get resolved by another technique. In this review paper our main objective is to do comparative study of clustering algorithms and issues arising during clustering process are also identified.

Keywords

Data mining Database Clustering k-means clustering Outliers 

References

  1. 1.
    R. Mythily, Aisha Banu, ShriramRaghunathan, Clustering Models for Data Stream Mining, Procedia Computer Science, Volume 46, 2015, Pages 619–626, ISSN 1877-0509.Google Scholar
  2. 2.
    Amineh Amini, Teh Ying, Hadi Saboohi, On Density-Based Data Streams Clustering Algorithms: A Survey, Journal of Computer Science and Technology, January 2014, Volume 29, Issue 1, pp 116–141.Google Scholar
  3. 3.
    Parul Agarwal, M. Afshar Alam, Ranjit Biswas, Issues, Challenges and Tools of Clustering Algorithm, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 3, No. 1, May 2011.Google Scholar
  4. 4.
    Hao Huang, Yunjun Gao, Kevin Chiew, Lei Chen, Qinming He, “Towards effective and efficient mining of arbitrary shaped clusters”, ICDE, 2014, 2014 IEEE 30th International Conference on Data Engineering (ICDE), 2014 IEEE 30th International Conference on Data Engineering (ICDE) 2014, pp. 28–39.Google Scholar
  5. 5.
    Kai Li, Xinxin Song, “A Fast Large Size Image Segmentation Algorithm Based on Spectral Clustering,” 2012 Fourth International Conference on Computational and Information Sciences, pp. 345–348.Google Scholar
  6. 6.
    A. Christy, G. Meera Gandhi, S. Vaithya subramanian, Cluster Based Outlier Detection Algorithm for Healthcare Data, Procedia Computer Science, Volume 50, 2015, Pages 209–215, ISSN 1877-0509.Google Scholar
  7. 7.
    Chih-Ping Wei, Yen-Hsien Lee and Che-Ming Hsu. Department of Information, Empirical Comparison of Fast Clustering Algorithms for Large Data Sets, Proceedings of the 33rd Hawaii International Conference on System Sciences – 2000.Google Scholar
  8. 8.
    Zhang Tie-jun, Chen Duo, Sun Jie, Research on Neural Network Model Based on Subtraction Clustering and Its Applications, Physics Procedia, Volume 25, 2012, Pages 1642–1647, ISSN 1875-3892.Google Scholar
  9. 9.
    Pedro Pereira Rodrigues, Joao Gama, Joao Pedro Pedroso, “Hierarchical Clustering of Time-Series Data Streams,” IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 5, pp. 615–627, May, 2008.Google Scholar
  10. 10.
    Zhensong Chen, Zhiquan Qi, Fan Meng, Limeng Cui, Yong Shi, Image Segmentation via Improving Clustering Algorithms with Density and Distance, Procedia Computer Science, Volume 55, 2015, Pages 1015–1022, ISSN 1877-0509.Google Scholar
  11. 11.
    Iurie Chiosa, Andreas Kolb, “Variational Multilevel Mesh Clustering,” Shape Modeling and Applications, International Conference on, pp. 197–204, 2008 IEEE International Conference on Shape Modeling and Applications, 2008.Google Scholar
  12. 12.
    Usue Mori, Alexander Mendiburu, and Jose A. Lozano, Member, Similarity Measure Selection for Clustering Time Series Databases, IEEE Transactions on Knowledge and Data Engineering, 2015.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Computer Engineering and TechnologyGuru Nanak Dev UniversityAmritsarIndia

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