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.