TAILIEUCHUNG - A fiction work on fuzzy based cancer gene identification through clustering

This paper proposed the cancer identification methods and evaluated them based on the computation time, classification accuracy and ability to reveal biologically meaningful gene information. This paper highlights the cancer gene identification using the Modified Fuzzy C-Means algorithm that is used efficiently in this work and examined with better results. | ISSN:2249-5789 V Sridevi et al , International Journal of Computer Science & Communication Networks,Vol 4(6),180-186 A FICTION WORK ON FUZZY BASED CANCER GENE IDENTIFICATION THROUGH CLUSTERING V. Sridevi 1, 2 1 Assistant Professor, Department of Computer Applications Dr. and Science College, Coimbatore-48 1 vissridevi@ 2 Research Scholar, Department of Computer Science, Dr. . Arts and Science College, Coimbatore-48. 2 vidhyacs67@ ABSTRACT: Cancer disease prediction is one of the rising research areas. Many algorithms such as K-Means, Kernel K-Means and Fuzzy C-Means are used to find the cancer affected genes in the sample dataset DLBCL, MLL, SRBTCM, and EWS. The above specified algorithms are not finding the cancer genes with more accuracy. So, the Modified Fuzzy C-Means algorithm is proposed to grasp the cancer genes. This paper proposed the cancer identification methods and evaluated them based on the computation time, classification accuracy and ability to reveal biologically meaningful gene information. This paper highlights the cancer gene identification using the Modified Fuzzy C-Means algorithm that is used efficiently in this work and examined with better results. continuous monitoring of thousands of gene expressions. With this large quantity of gene expression data, researchers started to search the possibilities of cancer detection using gene expression data. There are several methods have been proposed in recent years with good results. But there are still many issues which need to be clearly explained. In this paper, Modified Fuzzy CMeans algorithm is used to find the cancer affected genes in the sample dataset. While comparing the algorithms Modified Fuzzy C-Means, Kernel Based Clustering, the Modified Fuzzy C-Means is better. The Modified Fuzzy C-Means attains the merits of time concern and correct gene identification algorithm. The performance of these algorithms are also evaluated using the result

• Cơ sở dữ liệu
• International journal of computer science & communication networks
• Fiction work on fuzzy
• Cancer gene identification through clustering
• The Modified Fuzzy C Means algorithm
• The computation time
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• Distributed security system
• Ad hoc computer networks
• The Blackhole attack
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• Multi hop wireless mesh networks
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