An Improved Dual Clustering Method for Classification of Microarray Image Segementation
D. Ravi Kumar1, V. Devi2, M. Balasaraswathi3, B. Karthik4
1Dr. D. Ravi Kumar, Head, Department of Electronics and Communication Engineering, Vels Institute of Science, Technology &Advanced Studies (VISTAS), Chennai, India.
2Dr.V.Devi, Head, Department of Computer Applications, Gurunanak College, Chennai, India.
3Dr. M. Balasaraswathi, Associate Professor, Department of Electronics & Communication Engineering, Saveetha School of Engineering, SMITS, Chennai, India.
4Dr. B. Karthik, Associate Professor, Department of Electronics and Communication Engineering, Bharath Institute of Higher Education &Research, Chennai, India.

Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 2514-2519 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7117118419/2019©BEIESP | DOI: 10.35940/ijrte.D7117.118419

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Abstract: Microarray is a fast and rapid growing technology which plays dynamic role in the medical field. It is an advanced than MRI (Magnetic Resonance Imaging) and CT scanning (Computerised Tomography). The purpose of this work is to make fine perfection against the gene expression. In this study the two clustering are used which fuzzy c means and k means and also it classifies with better results. The microarray data base indicates the classification in support vector machine. Segmentation is most important step in microarray image. The classification in support vector machine is compared with other two classifiers which means the k nearest neighbour and with the Bayes classifiers.
Keywords: Segmentation, Microarray Image, Classification (SVM – Support Vector Machine), Dual Clustering.
Scope of the Article: Classification.