Figure 3

Scheme of the spectral clustering methodology. Spectral clustering techniques aim to find the best partition of a weighted graph. A graph is constructed where the nodes are MF-GO terms linked by similarity values s _ij derived by calculating the cosine distance between the vectors of the co-occurrence matrix. The similarity matrix S = [s _ij ] is treated as a real-value adjacency matrix of the graph. Let P be a normalized matrix named the Transition Probability matrix that represents the probability of transit from one node to another in this weighted graph. P is calculated from S. The first K eigenvalues of P are used to map the nodes of the graph to a K-dimensional space and the points in this reduced space can be grouped by any clustering algorithm. In this work, we have applied a hierarchical clustering algorithm.