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Improvement of Pre-processing Capacity of Support Vector Clustering using Neural Network Kernel Function for Stream Data Classification

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 6
Year of Publication: 2014
Authors:
Ritika Chatterjee
Shweta Shrivastav
Vineet Richhariya
10.5120/16798-6511

Ritika Chatterjee, Shweta Shrivastav and Vineet Richhariya. Article: Improvement of Pre-processing Capacity of Support Vector Clustering using Neural Network Kernel Function for Stream Data Classification. International Journal of Computer Applications 96(6):19-22, June 2014. Full text available. BibTeX

@article{key:article,
	author = {Ritika Chatterjee and Shweta Shrivastav and Vineet Richhariya},
	title = {Article: Improvement of Pre-processing Capacity of Support Vector Clustering using Neural Network Kernel Function for Stream Data Classification},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {6},
	pages = {19-22},
	month = {June},
	note = {Full text available}
}

Abstract

Pre-processing of data before generation of pattern or classification is major steps. In the phase of pre-processing reduces the noise level of data using different technique of data mining. In current research trend support vector clustering is used for efficient data processing for noise reduction and pattern generation. Support vector clustering is new paradigm of data mining tools. It combined with supervised learning and unsupervised learning. for the success story behind support vector clustering technique is kernel function. The better selection of kernel function produces better result in terms of noise reduction and classification. In this paper proposed an improved support vector clustering method using neural network kernel function for stream data classification. The neural network function work as data optimizer and data selector in support vector clustering.

References

  • Chang-Dong Wang, Jian Huang La, Dong Huang, Dong Huang "SVStream: A Support Vector-Based Algorithm for Clustering Data Streams" IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 25, 2013. Pp 1410-1425.
  • Xin Xu, Wei Wang, Guilin Zhang, Yongsheng Yu "An Adaptive Feature Selection Method for Multi-class Classi?cation" 2010. Pp 225-230.
  • A. Ben-Hur, D. Horn, H. T. Siegelmann, V. Vapnik, "A Support Vector Clustering Method", In Proc. of Int. Conf. on Pattern Recognition, 2000, pp. 724-727.
  • J. Saketha Nath, S. K. Shevade, "An Efficient Clustering Scheme Using Support Vector Methods", Pattern Recognition, 2006, 1473-1480.
  • J. S. Wang, J. C. Chiang, "A Cluster Validity Measure with a Hybrid Parameter Search Method for Support Vector Clustering Algorithm", PatternRecognition, 2008, pp. 506-520.
  • J. S. Wang, J. C. Chiang, "An Efficient Data Preprocessing Procedure for Support Vector Clustering", Journal of Universal Computer Science, 2009, pp. 705-721.
  • J. Lee, D. Lee, "An Improved Cluster Labeling Method for Support Vector Clustering", IEEE Trans. Pattern Analysis and Machine Intelligence, 2005, pp. 461-464
  • Jiawei Han and Micheline Kamber, "Data Mining: Concepts and Techniques", Morgan Kaufmann Publishers, Second Edition, 2006, pp. 355.
  • L. Ertoz, M. Steinbach, V. Kumar, "Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data", In Proc. of SIAM Int. Conf. on Data Mining, 2003, pp. 1-12.
  • R. A. Jarvis, E. A. Patrick, "Clustering Using a Similarity Measure Based on Shared Nearest Neighbors", IEEE Trans. Computers, C-22, 11, 1973, pp. 1025-1034.
  • J. S. Wang, J. C. Chiang, "A Cluster Validity Measure with Outlier Detection for Support Vector Clustering", IEEE Trans. Systems, Man, and Cybernetics-Part B, 38, 1, 2008, pp. 78-89.
  • J. Yang, V. E. Castro, S. K. Chalup, "Support Vector Clustering Through Proximity Graph Modeling", In Proc. of 9th Int. Conf. on Neural Information Processing, 2002, pp. 898-903.
  • D. Tax and R. Duin, "Support vector domain description", Pattern Recognition Letters, vol. 20, 1999, pp. 1191-1199.