The current world of internet, mobile devices, businesses, social media platforms, healthcare systems, and the Internet of Things all have a lot of data available online. In the study of genetic datasets, clustering is crucial. In the gene dataset, there is a tremendous quantity of hidden information. To uncover hidden information in gene expression data, a deeper understanding of functional genomics is required. Working with the gene dataset presents a lot of obstacles. The enormous volume of data, dimensionality, and dataset changes throughout time are these problems. Clustering algorithms are a useful tool for solving this type of problem. Consequently, the first step in resolving these issues is the application of clustering algorithms, which are necessary for data mining procedures to reveal the structure and hidden patterns in gene datasets. Gene expression datasets are analysed using several clustering techniques. Five clustering algorithms—OPTICS (Ordering Points To Identify Clustering Structure), Hierarchical Clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and HDBSCAN—have been used in this study to cluster the cancer gene database. The efficiency of each clustering approach is assessed using a range of external and internal parametric clustering assessment metrics. When compared to other clustering approaches, the OPTICS and DBSCAN clustering algorithm yields the best results.