Modification of optimum-path forest using Markov cluster process algorithm

Optimum-path forest (OPF) is a novel supervised graph-based classifier which reduces the classification problem into partitioning of vertices in a graph derived from the data samples. One of the main processes in OPF is identifying the optimum set of key samples named prototypes. This process is based on creating a minimum spanning tree on a complete weighted graph which is derived from the training samples; hence, it is much time-consuming for large-scale problems. In this study, for overcoming this limitation, the process of finding the prototypes in traditional OPF is modified by using Markov cluster (MCL) algorithm. The graph partitioning in MCL is based on finding key samples named attractors, which attract other related samples; so the obtained attractors can be selected as prototypes for generating optimum-path trees. Experiments on public benchmark datasets show that the speed of proposed modified OPF is improved considerably as compared to the traditional OPF.