Clustering and overlapping modules detection in PPI network based on IBFO

As is known to all, traditional clustering algorithms do not work well due to the topological features of protein-protein interaction networks. An improved clustering method based on bacteria foraging optimization (BFO) mechanism and intuitionistic fuzzy set, short for improved BFO, is proposed in this paper, in which the trigonometric function is used to define the membership degrees and the indeterminacy degree is introduced to detect the overlapping modules. In chemotactic operation of BFO, the algorithm initializes a cluster center according to comprehensive network feature value of node and eliminates the isolated point in accordance with edge-clustering coefficient. In the reproduction operation of BFO, the nodes possessing high membership degrees are merged into the cluster that the cluster center belongs to and labeled as visited nodes. Meanwhile, the nodes that also have high indeterminacy degrees are visited again when generating another cluster. The procedure of elimination-dispersal operation is equivalent to the selection of the next cluster center. Finally, the algorithm merges the clusters having high similarity. The results show that the algorithm not only determines the cluster number automatically, improves the f-measure value of cluster results, but also identify the overlaps in protein-protein interaction network successfully.