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Present studies, development of genomic technologies are highly concentrated on galactic scale gene data. In Bioinformatics community, the sizable volume of gene data investigation and distinguishing the behavior of genes in antithetical conditions are the intriguing task. This cognitive factor can be deal by the clustering technique, its groups the similarity patterns at various features. Moreover, gene expression data indicates the contrastive levels of gene behaviors in various tissue cells and it does provide the feature information effectively. This gene clustering investigation is precise and accommodating in cancer uncovering because of its easiness to detect the cancerous and non-cancerous genes. The precautionary measures cancer diagnostic is precise crucial for cancer prevention and treatment. The existing cancer gene clustering techniques includes several limitations such as time complexities in training and testing samples, maximum redundant features and high dimensional data. These issues are severely influences the data clustering accuracy. This paper focuses on survey of various clustering techniques of cancer gene clustering with respect to cancer gene benchmark datasets. Furthermore, review of existing cancer gene clustering technique describes the advantages and limitations comprehensively.

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