graph-clustering
In Recent days, Biological data is often represented as networks, as in the case of protein-protein interactions and metabolic pathways. Modeling, analyzing, and visualizing networks can help make sense of large volumes of data generated by high throughput experiments.
However, due to their size and complex structure, biological networks can be difficult to interpret without further processing. Cluster analysis is a widely-used approach to extract meaningful information from biological networks.
Clustering algorithms are methods used to cluster the data into different groups based on a similarity measure. The goal of the project is to implement some of the widely used clustering algorithms which are suitable for clustering biological data and analyze the effectiveness of the algorithm for a particular dataset like PPI and thereby assessing the strength and weakness of the algorithm in the context of molecular networks.