Data Mining and its Clustering Techniques : A Review

Authors(1) :-Sakshi

Data mining is the arrangement of the extraction of the concealed example from the records to be had. Differing class techniques were completed in records mining way. Those approaches have been utilized to separate the realities into extraordinary sets all together that effectively connection between select traits can be analyzed. Distinctive realities mining strategies have been utilized to help wellbeing mind specialists inside the examination of diabetes affliction. The ones frequently utilized acknowledgment on type: credulous Bayes choice tree, and neural system. Distinctive data mining strategies additionally are utilized which incorporates bit thickness, mechanically depicted associations, sacking calculation, and help vector framework. The issue of repetition in is persistently happened. In our artworks we will reduce this problem.

Authors and Affiliations

Sakshi
Assistant Professor, Department of Computer Science and Applications, Guru Nanak College, Ferozepur Cantt, Punjab, India

Data Mining, clustering, KNN, Fuzzy-KNN, Naïve Bayes, Neural Network, SupportVector Machine.

[1] MihaelAnkerst, Markus M. Breunig, Hans-Peter Kriegel, and Jor ¨ g Sander. OPTICS: OrderingPoints To Identify the Clustering Structure. In Proceedings of the International Conference on Management of Data, (SIGMOD), volume 28(2) of SIGMOD Record, pages 49–60, Philadelphia, PA,USA, 1–3 June 1996. ACM Press.

[2] Michael R. Anderberg. Cluster analysis for applications. Academic Press, 1973.

[3] Anil K. Jain and Richard C. Dubes. Algorithms for Clustering Data.Prentice-Hall, 1988.

[4] Matthias Jarke, Maurizio Lenzerini, YannisVassiliou, and PanosVassiliadis.Fundamentals of

Data Warehouses.Springer, 1999.

[5] Jon M. Kleinberg. Authoritative Sources in a Hyperlinked Environment. In Proceedings of the

9th Annual ACM-SIAM Symposium on 

Discrete Algorithms, pages 668–677, San Francisco, CA,

USA, 25–27 January 1998. ACM Press.

[6] VassiliosTzerpos and Richard C. Holt.MoJo: A Distance Metric for Software Clustering. In

Proceeedings of the 6th Working Conference on Reverse Engineering, (WCRE), pages 187–195, Atlanta, GA, USA, 6–8 October 1999. IEEE Press.

[7] Wei Wang, Jiong Yang, and Richard R. Muntz. STING: A Statistical Information Grid Approach to Spatial Data Mining. In Proceedings of the 23rd International Conference on Very Large Data Bases,(VLDB), pages 186–195, Athens, Greece, 26–29 August 1997. Morgan Kaufmann Publishers.

[8]Sankaranarayanan, S. “Diabetic Prognosis through Data Mining Methods and Techniques”, International Conf. on Intelligent Computing Applications (ICICA), 2014, pp. 162 – 166.

[9] C. M. Velu “Visual Data Mining Techniques for Classification of Diabetic Patients”, IEEE Conf. on Advance Computing Conference (IACC), 2013, pp. 1070 – 1075.

[10] Wang, Guoyin “Granular computing based data mining in the views of rough set and fuzzy set” IEEE Conf. on Granular Computing, 2008, PP 67.

[11] Jagannathan, G. “Seventh IEEE International Conference on Data Mining Workshops”, IEEE Conf. on Data Mining Workshops, 2007, pp. 1– 3

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Publication Details

Published in : Volume 4 | Issue 7 | March-April 2018
Date of Publication : 2018-03-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 314-317
Manuscript Number : IJSRST184850
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Sakshi, " Data Mining and its Clustering Techniques : A Review", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 4, Issue 7, pp.314-317, March-April-2018.
Journal URL : http://ijsrst.com/IJSRST184850

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