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A Survey: Clustering Algorithms in Data Mining

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IJCA Proceedings on Innovations in Computing and Information Technology (Cognition 2015)
© 2015 by IJCA Journal
COGNITION 2015 - Number 2
Year of Publication: 2015
Authors:
Sonamdeep Kaur
Sarika Chaudhary
Neha Bishnoi

Sonamdeep Kaur, Sarika Chaudhary and Neha Bishnoi. Article: A Survey: Clustering Algorithms in Data Mining. IJCA Proceedings on Innovations in Computing and Information Technology (Cognition 2015) COGNITION 2015(2):12-14, July 2015. Full text available. BibTeX

@article{key:article,
	author = {Sonamdeep Kaur and Sarika Chaudhary and Neha Bishnoi},
	title = {Article: A Survey: Clustering Algorithms in Data Mining},
	journal = {IJCA Proceedings on Innovations in Computing and Information Technology (Cognition 2015)},
	year = {2015},
	volume = {COGNITION 2015},
	number = {2},
	pages = {12-14},
	month = {July},
	note = {Full text available}
}

Abstract

In data mining Clustering is a technique that's aims to single out the data elements into different clusters based on useful features. In this technique data elements that are similar to one another are placed within the same cluster and those which are dissimilar are placed in different clusters. Many algorithms have been proposed in the literature but the most active research algorithms are unsupervised clustering methods of data mining:Partitioning and Hierarchical Methods for clustering. The choice of a particular clustering method depends on many factors or themes. The key idea of this paper is categorizing the methods on the bases of different themes so that it helps in choosing algorithms for any further improvement and optimization.

References

  • Stefano Serafin, AlessionBerto, Dino Zardi. (2005) Application of Cluster Analysis Technique to the Verification of Quantitative Precipitation Forecasts. University of Trento.
  • Shalini. S. Singh, N. C. Chauhan (2011). K-Mean v/s K-Medoids: A comparative study. National Conference on Recent Trends in Engineering & Technology.
  • Dr. T. Velemurugan. (2012) Efficiency of k-Means and K-Medoids Algorithms for Clustering Arbitrary Data Points. ISSN: 2229-6093.
  • George Karypis, Eui-Hong (Sam) Han, Vipin Kumar. (1999). CHAMELEON:AHierarchical ClusteringAlgorithm. Using Dynamic Modelling.
  • Guha, Sudipto; Rastogi, Rajeev; Shim, Kyuseok (2001). "CURE: An Efficient Clustering Algorithm for Large Databases".
  • Pooja Gupta,, Monika Jena. (2013). Comparative study of different clustering algorithms for association rule mining.
  • Jiawei Han and MichelineKamber. (2006). Data Mining: Concepts and Techniques.
  • Malwindersingh,Meenakshibansal. (2014). Survey On Clustering And Optimization Techniques ToDevelop Hybrid Clustering Technique International Journal Of Computer Engineering and Applications.