Contrasting and Evaluating Different Clustering Algorithms: A Literature Review
Keywords:
Clustering, K-Means Algorithm, Hierarchical Clustering Algorithm, K-Medoids Algorithm, Density Based AlgorithmAbstract
Clustering is a practice of splitting data into set of analogous objects; these sets are identified as clusters. Each cluster comprised of points that are alike among them and unalike compared to points of other cluster. This paper is being set to study and put side by side different data clustering algorithms. The algorithms under exploration are: k-means algorithm, hierarchical clustering algorithm, k-medoids algorithm, and density based algorithms. All these algorithms are analyzed on R-tool by taking same data-set under observation.
References
Caiming Zhong ,Duoqian Miao,” Minimum spanning tree based split-and-merge: A hierarchical clustering method”, Journal of Information Sciences, Volume 181 Issue 16,August 2011, Elsevier ScienceInc.New York,USA,pages:3397-3410.
Anuradha Awachar, Rajashree Bairagi, Vijayalaxmi Hegade and Mahadev Khandagale, "An Overview of Ontology Based Text Document Clustering Algorithms", International Journal of Computer Sciences and Engineering, Volume-02, Issue-02, Page No (60-64), Feb -2014.
Xindong Wu · Vipin Kumar · J. Ross Quinlan,” Top 10 algorithms in data mining”, International Conference on Data Mining (ICDM) in December 2006.
A. K. Jain and R. C. Dubes. “Algorithms for Clustering Data.” Prentice-Hall, Englewood Cliffs, NJ, 1988.
L. Kaufman and P.J. Rousseeuw. “Finding Groups in Data: An Introduction to Cluster Analysis.” Wiley, New York, 1990.
S.Anitha Elavarasi and Dr. J. Akilandeswari and Dr. B. Sathiyabhama, January 2011, A Survey On Partition Clustering Algorithms.
F. Murtagh. A survey of recent advances in hierarchical clustering algorithms.Computer Journal, 26(4):354–359, 1983.
W. Day and H. Edelsbrunner., “Efficient algorithms for agglomerative hierarchical clustering methods”. Journal of Classification, 1(7):7–24, 1984.
S. Guha, R. Rastogi, and K. Shim, 1998. CURE: An Efficient Clustering Algorithm for Large Databases. Proc. ACM Int’l Conf. Management of Data : 73-84.
J. Hartigan and M. Wong. Algorithm as136: A k-means clustering algorithm. Applied Statistics, 28:100–108, 1979.
Kilian Stoffel and Abdelkader Belkoniene “Parallel k/h-Means Clustering for Large Data Sets”, P. Amestoy et al. (Eds.): Euro-Par'99, LNCS 1685, pp. 1451{1454, 1999.c Springer-Verlag.
Zha, H., Ding, C., Gu, M., He, X., & Simon, H. (2002)“Spectral relaxation for K-means clustering.” Advances in Neural Information Processing Systems 14 (NIPS’01),1057–1064.
Raymond T. Ng and Jiawei Han.,” CLARANS: A Method for Clustering Objects for Spatial Data Mining. “IEEE Transactions on Knowledge and Data Engineering, 14(5):1003{1016, 2002.
L. Kaufman and P. J. Rousseeuw. “Finding Groups in Data: an Introduction to Cluster Analysis”. John Wiley & Sons,1990
R. T. Ng and J. Han. ,”Efficient and Effective clustering methods for spatial Data Mining”, Proc. of the 20th Int’l Conf.on Very Large Databases, Santiago, Chile, pages 144–155,1994.
D.Napoleon , P.Ganga Lakshmi,” An Enhanced k-means algorithm to improve the Efficiency Using Normal Distribution Data Points “,(IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 07, 2010, 2409-2413.
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