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Gene expression data hide vital information required to understand the biological process that takes place in a particular organism. Extracting the hidden patterns in gene expression data helps to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, thousands of gens can be analyzed at a time  using clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to understand  natural structures and identify interesting patterns in the underlying gene expression  data [2]. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, finding useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in drug   design. Clustering is a useful method that groups items based on certain similarity measures for understanding the structures, functions, regulation of genes, and cellular processes obtained from gene expression data and providing more insight on a given data set [13]. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.