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Mining Patterns for Clustering Using Modified K-Means and SVM (Support Vector Machine)


Affiliations
1 NorthCap University, Gurgaon, India
     

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Data mining can be termed as a process of extracting patterns (knowledge) and posing query from data. Stored in database. Classification is one among of its concept and techniques.  This research article is proposing a novel hybrid mining approach by using modified K-Means and Support vector machine algorithm. Modified K-Means utilized here for making the clusters from given dataset and SVM is utilized for classification (on clustered dataset obtained from modified K-means clustering). Experiments are performed over different datasets which are taken from UCI repository. Datasets which are used for comparing clustering algorithm are provided in Table 1 along with their details. Evaluations are done on different datasets of following parameters: Accuracy obtained from new algorithm and confusing matrix which is being created for every dataset. Additionally, proposed algorithms provide better result than other.


Keywords

Confusion Matrix, Clustering, K-Means, Modified K-Means, SVM (Support Vector Machine).
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  • A. E Gutierrez-Rodriguez, J. Fco Martinez – Trinidad, M. Garcia-Borroto, J.A. Carrasco-Oacha,” Mining patterns for clustering on numerical datasets using unsupervised decision tree”, Knowledge based systems, Pp. 70-79, 82(2015).

  • R.S Michalski, R.E Stepp, “Automated constructions of classifications; conceptual clustering versus numerical taxonomy”, IEEE Trans. Pattern Anal. Machine Learn. Pp. 396-410, 5(4) (1983).

  • Bing Liu, Yiyuan Xia and Philip S. Yu,” Clustering through Decision Tree Construction”, (CIKM-2000), Washington DC, USA, November 6-11, 2000.

  • Daxin jiang, Jai pie (CANADA), Aidong Zhang (USA), “General approach to mining quality based clustering on microarray data “, L. Zhou, B.C. Ooi, and X. Meng (Eds.): DASFAA 2005, LNCS 3453, pp. 188–200, Springer-Verlag Berlin Heidelberg 2005.

  • Vivekanathan. P,” Different data mining algorithm: A Performance Analysis” Volume 1, Issue 3, Pp. 79-84, September – October 2012.

  • Muhammad Ali Masood, M. N. A. Khan, "Clustering Techniques in Bioinformatics", I.J. Modern Education and Computer Science, vol. 1. Pp. 38-46, January 2015.

  • K. Hanumantha Rao, G. Srinivas, Ankam Damodhar and M. Vikas Krishna, “Implementation of Anomaly Detection Technique Using Machine Learning Algorithms", International Journal of Computer Science and Telecommunications, Volume 2, Issue 3, Pp. 25-31, June 2011.

  • Sumit Garg, Arvind K. Sharma, "Near Analysis of Data Mining Techniques on Educational Dataset”, International Journal of Computer Applications (0975 – 8887), Volume 74– No.5, July 2013.

  • Lior Rokach, Oded Maimon(Eds.), "Clustering Methods", Data Mining and Knowledge Discovery Handbook, XXXVI, 1383 Pp.400 illus., Hardcover ISBN:0-387-24435-2, Springer, 2005.

  • Pavel Berkhin, “Survey of Clustering Data Mining Techniques”, Grouping multidimensional data-recent advances in clustering, ISBN 9873-3-540-28348-5, Pp. 25-71, Springer 2006.

  • Muhammet Mustafa Ozdal, Cevdet Aykanat” Hypergraph Models and Algorithms for Data-Pattern-Based Clustering”, Data Mining and Knowledge Discovery, Kluwer Academic Publishers. Manufactured in The Netherlands 9, Pp.29–57, 2004.

  • Mythilli, Madhiya, “An Analysis on Clustering Algorithms in Data Mining”. CSMC, Vol. 3, Issue. 1, Pp. 334-340, January 2014.

  • Arthur Zimek, Ira Assent and Jilles Vreeken, “Frequent Pattern Mining Algorithms for Data Clustering”, DOI 10.1007/978-3-319-07821-2_16, Pp. 403-423, Springer International Publishing Switzerland 2014.

  • K.Tamizharasi, Dr. UmaRani, K.Rajasekaran, “Performance analysis of various data mining algorithms”, International Journal of Computing Communication and Information System (IJCCIS), Vol.6. No-3, Pp. 118-127, July-September 2014.

  • Jiangping Chen, Ting Hu, Pengling Zhang, Wenzhong Shi, “Trajectory clustering for people’s movement pattern based on crowd souring data”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-2, 2014, ISPRS Technical Commission II Symposium, Toronto, Canada, 6 – 8 October 2014.

  • P. Keerthana, P. Thamilselvan, J.G.R. Sathiaseelan, “Performance Analysis of Data Mining Algorithms for Medical Image Classification”, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.3, Pp. 604-609, March- 2016.

  • M.S. Mythili, A.R. Mohamed Shanavas, Ph. D, “Performance Evaluation of Apriori and FP-Growth Algorithms”, International Journal of Computer Applications (0975 – 8887) Volume 79 – No10, Pp. 34-37, October 2013.


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  • Mining Patterns for Clustering Using Modified K-Means and SVM (Support Vector Machine)

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Authors

Bhawana Yadav
NorthCap University, Gurgaon, India
Anuradha
NorthCap University, Gurgaon, India
Yogita Gigras
NorthCap University, Gurgaon, India

Abstract


Data mining can be termed as a process of extracting patterns (knowledge) and posing query from data. Stored in database. Classification is one among of its concept and techniques.  This research article is proposing a novel hybrid mining approach by using modified K-Means and Support vector machine algorithm. Modified K-Means utilized here for making the clusters from given dataset and SVM is utilized for classification (on clustered dataset obtained from modified K-means clustering). Experiments are performed over different datasets which are taken from UCI repository. Datasets which are used for comparing clustering algorithm are provided in Table 1 along with their details. Evaluations are done on different datasets of following parameters: Accuracy obtained from new algorithm and confusing matrix which is being created for every dataset. Additionally, proposed algorithms provide better result than other.


Keywords


Confusion Matrix, Clustering, K-Means, Modified K-Means, SVM (Support Vector Machine).

References