Volume 25, Issue 1, June 2016, Pages 308–319
N. MAGENDIRAN1 and S. SELVARAJAN2
1 Associate Professor / CSE, Paavai Engineering College, Namakkal, Tamilnadu, India
2 Principal, Muthayammal College of Engineering, Rasipuram, Tamilnadu, India
Original language: English
Copyright © 2016 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The breast cancer is the most threatening factor of women’s lifestyle and the reason of the disease has many factors, but still the gene factor has more influence in the generation of breast cancer where the early diagnosis and prevention is essential. There are many approaches has been discussed in the literature, but the identification and selection of a set of genes which influence the disease is still complicated one. We propose a multi variant approach for gene selection which is performed by performing high dimensional subspace clustering. With the given data set, the method generates a set of rules and unlike generic fuzzy rules the method splits the range values into the number of parts and based on that the rules are generated. Also, according to the different range values, the method generates a multi gene impact matrix where the frequency of range values of each rule is stored. The data set is clustered according to the generated rules and from the generated rules the gene selection is performed. For the gene selection, we compute the multi gene frequency measure which represents how depth the gene has an impact on the classification of disease. The proposed method produces efficient classification of genes in the influence of breast cancer and produces efficient results.
Author Keywords: Gene Selection, High Dimensional Clustering, Multi Gene Impact Matrix, Fuzzy Rule Sets.
N. MAGENDIRAN1 and S. SELVARAJAN2
1 Associate Professor / CSE, Paavai Engineering College, Namakkal, Tamilnadu, India
2 Principal, Muthayammal College of Engineering, Rasipuram, Tamilnadu, India
Original language: English
Copyright © 2016 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The breast cancer is the most threatening factor of women’s lifestyle and the reason of the disease has many factors, but still the gene factor has more influence in the generation of breast cancer where the early diagnosis and prevention is essential. There are many approaches has been discussed in the literature, but the identification and selection of a set of genes which influence the disease is still complicated one. We propose a multi variant approach for gene selection which is performed by performing high dimensional subspace clustering. With the given data set, the method generates a set of rules and unlike generic fuzzy rules the method splits the range values into the number of parts and based on that the rules are generated. Also, according to the different range values, the method generates a multi gene impact matrix where the frequency of range values of each rule is stored. The data set is clustered according to the generated rules and from the generated rules the gene selection is performed. For the gene selection, we compute the multi gene frequency measure which represents how depth the gene has an impact on the classification of disease. The proposed method produces efficient classification of genes in the influence of breast cancer and produces efficient results.
Author Keywords: Gene Selection, High Dimensional Clustering, Multi Gene Impact Matrix, Fuzzy Rule Sets.
How to Cite this Article
N. MAGENDIRAN and S. SELVARAJAN, “MULTI VARIANT GENE SELECTION APPROACH BASED HIGH DIMENSIONAL SUB SPACE CLUSTERING OF BREAST CANCER DATA SET FOR EFFICIENT CLASSIFICATION USING FUZZY RULE SETS AND MULTI GENE IMPACT MATRIX,” International Journal of Innovation and Scientific Research, vol. 25, no. 1, pp. 308–319, June 2016.
