Polynomial Kernel Function based Support Vectors for Data Stream Clustering
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International Journal of Computer & Organization Trends (IJCOT) | 
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| © 2014 by IJCOT Journal | ||
| Volume - 4 Issue - 6 |
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| Year of Publication : 2014 | ||
| Authors : Bethapudi Naga Raju, Amarnath Gadham | ||
| DOI : 10.14445/22492593/IJCOT-V15P307 |
Citation
Bethapudi Naga Raju, Amarnath Gadham "Polynomial Kernel Function based Support Vectors for Data Stream Clustering", International Journal of Computer & organization Trends (IJCOT), V4(6):12-18 Nov - Dec 2014, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.
Abstract
Support vector clustering (SVC) is an important clustering algorithm based on support vector machine (SVM) and kernel methods. SVC algorithm performed better than the other traditional clustering methods, such as a global optimum, treatment of data sets of arbitrary shape, no need for specifying the number of clusters, fewer parameters, and easy treatment of high dimensional data. SV clustering consists of two phases, training based support vector machine and labeling clusters. Training phase allowing for bounded support vectors (BSVs), the existing SVStream algorithm is capable of identifying overlapping clusters. A BSV decaying mechanism is designed to automatically detect and remove outliers (noise). But outlier data doesn’t optimized using linear kernel function. Proposed system will use polynomial kernel function to efficiently estimate support vectors and eliminates irrelevant data points. We represent an alternative technique for clustering stream data by using the SVM(Support Vector Machine) method. Streaming Data objects are mapped to a high dimensional characteristic space, in which support-vectors are describe a arbitrary shape training them. The outer region of the data points forms in n-dimensional space a definitive set of closed contours including the data. Streaming kdd data objects are surrounded by each contour are defined as a cluster. Experimental results show proposed method outperformed well against outliers and noise handling over existing methods. This system achieves high accuracy detection rate and less error rate of KDD CUP 1999 training data set.
References
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