Review of data mining clustering techniques to analyze data with high dimensionality as applied in gene expression data

From oncology science, the uncontrolled growth of malignant/benign tumours refers to secreted reasons causing the formation of new blood vessels sprouting from pre-existing vessels. Consequently, scientists attribute this abnormal behaviour to intratumour factors, defined as tumour-derived factors. These factors are guided through protein molecules that work on cellular signalling path. Accordingly, the deoxyribonucleic acid (DNA) is considered as the maestro of this process. Analysing changes on the gene expression may give rise for diagnosis enhancement of affected tissues in their early stages. Hence, an ongoing research is addressing the problem of subspace clustering methodologies suitable for high dimensional datasets, particularly suitable for the analysis of gene expression data. In this context, researchers have identified various limitations of these methods particularly in the areas of information integration systems, text-mining and bio-informatics. This paper aims at providing an overview of the published literature with a particular focus on the current status of subspaces clustering for knowledge discovery toward tumour diagnosis. This is considered to be an essential step in attempt to overcome the limitations and provide effective statistical model in sense of genetic knowledge discovery.