PPI modules detection method through ABC-IFC algorithm

A novel clustering model is proposed which combines the optimization mechanism of artificial bee colony (ABC) with the fuzzy membership matrix in this paper. The clustering model contains two parts: one is to search optimum cluster centers using ABC mechanism, the other is to implement clustering using intuitionistic fuzzy clustering (IFC) method. Firstly, the cluster centers are set randomly and the initial clustering results are obtained using fuzzy membership matrix. The new cluster centers are updated with the nodes that contain the maximal amount of information in the previous clusters of onlookers by ABC algorithm. If the onlookers are incapable of updating, the scouts will generate new cluster centers via global searching. Then the clustering result is obtained through IFC method based on the new optimized cluster centers. Considering that some protein nodes in PPI networks are unreachable, which leads to the traditional distance based clustering criteria infeasible. Therefore the new objective function is designed. The improved algorithm, named ABC-IFC, is also compared with the traditional fuzzy C-means clustering and IFC method. The experimental results on MIPS dataset show that the new algorithm does not only get improved in terms of several commonly used evaluation criteria such as precision, recall and P-value, but also obtains a better clustering result.