Evolving technologies have made it cost-effective to rapidly collect diverse types of biological data. Data from genomics, transcriptomics, epigenomics, and other types of omics technologies offer an opportunity to investigate and to understand underlying biological processes. For example, in health research, this type of data may be used for precise diagnosis of diseases and in agriculture, this type of data may be used to understand growth differences among crop varieties. One approach towards this type of data analysis is clustering. This presentation will, first, focus on clustering of individual datasets arising from omics technologies. Next, the presentation will focus on clustering of combined data from multi-omics technologies, including epigenomics and transcriptomics. Challenges associated with each method will be discussed.