In data mining, cluster analysis is used to classify a set of observations into two or more mutually exclusive unknown groups, based on combinations of the interval variables. The purpose is to discover a system of organizing observations, usually genes, and proteins into groups, where members of the groups share properties in common. In Creative Proteomics, we can interpret the data you collected with a set of typical clustering methodologies, algorithms, and applications, which include partitioning methods such as k-means, hierarchical methods and density-based methods. Your data can be interpreted and visualized with our assistance.