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Reseach Article

A Survey: Clustering Algorithms in Data Mining

Published on July 2015 by Sonamdeep Kaur, Sarika Chaudhary, Neha Bishnoi
Innovations in Computing and Information Technology (Cognition 2015)
Foundation of Computer Science USA
COGNITION2015 - Number 2
July 2015
Authors: Sonamdeep Kaur, Sarika Chaudhary, Neha Bishnoi
71df9bff-5ac3-42c2-8a71-ee593a5bcb5b

Sonamdeep Kaur, Sarika Chaudhary, Neha Bishnoi . A Survey: Clustering Algorithms in Data Mining. Innovations in Computing and Information Technology (Cognition 2015). COGNITION2015, 2 (July 2015), 12-14.

@article{
author = { Sonamdeep Kaur, Sarika Chaudhary, Neha Bishnoi },
title = { A Survey: Clustering Algorithms in Data Mining },
journal = { Innovations in Computing and Information Technology (Cognition 2015) },
issue_date = { July 2015 },
volume = { COGNITION2015 },
number = { 2 },
month = { July },
year = { 2015 },
issn = 0975-8887,
pages = { 12-14 },
numpages = 3,
url = { /proceedings/cognition2015/number2/21893-2122/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Innovations in Computing and Information Technology (Cognition 2015)
%A Sonamdeep Kaur
%A Sarika Chaudhary
%A Neha Bishnoi
%T A Survey: Clustering Algorithms in Data Mining
%J Innovations in Computing and Information Technology (Cognition 2015)
%@ 0975-8887
%V COGNITION2015
%N 2
%P 12-14
%D 2015
%I International Journal of Computer Applications
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
  1. Stefano Serafin, AlessionBerto, Dino Zardi. (2005) Application of Cluster Analysis Technique to the Verification of Quantitative Precipitation Forecasts. University of Trento.
  2. Shalini. S. Singh, N. C. Chauhan (2011). K-Mean v/s K-Medoids: A comparative study. National Conference on Recent Trends in Engineering & Technology.
  3. Dr. T. Velemurugan. (2012) Efficiency of k-Means and K-Medoids Algorithms for Clustering Arbitrary Data Points. ISSN: 2229-6093.
  4. George Karypis, Eui-Hong (Sam) Han, Vipin Kumar. (1999). CHAMELEON:AHierarchical ClusteringAlgorithm. Using Dynamic Modelling.
  5. Guha, Sudipto; Rastogi, Rajeev; Shim, Kyuseok (2001). "CURE: An Efficient Clustering Algorithm for Large Databases".
  6. Pooja Gupta,, Monika Jena. (2013). Comparative study of different clustering algorithms for association rule mining.
  7. Jiawei Han and MichelineKamber. (2006). Data Mining: Concepts and Techniques.
  8. Malwindersingh,Meenakshibansal. (2014). Survey On Clustering And Optimization Techniques ToDevelop Hybrid Clustering Technique International Journal Of Computer Engineering and Applications.
Index Terms

Computer Science
Information Sciences

Keywords

Partitional Clustering Hierarchical Clustering statistical Methods k-means birch Cure.