Description: The term ‘bicluster’ refers to a group of objects that share similarities in two different dimensions. This concept is used in various disciplines, such as statistics, biology, and computer science, to identify patterns and relationships among elements. In the field of data analysis, a bicluster can be represented as a submatrix where rows and columns represent different characteristics or variables, highlighting the relationships that exist among a specific subset of data. Identifying biclusters allows researchers and analysts to discover groupings that are not immediately apparent, facilitating the understanding of the underlying structure of the data. The main characteristics of a bicluster include homogeneity within each group and heterogeneity between different groups, making it a valuable tool for exploratory analysis. Additionally, the concept of biclustering relates to the idea that data can be organized in multiple ways, allowing for a richer and more nuanced view of the information. In summary, biclustering is a powerful approach for data analysis that helps unravel the complexity of relationships among multiple variables in a dataset.
