Parallelization of K-medoid clustering algorithm

This paper presents an approach for paralleling K-medoid clustering algorithm. The K-medoid algorithm will be divided into tasks, which will be mapped into multiprocessor system. The control structure for the way of expressing the tasks in parallel form and the communication model that satisfied the mechanism for interaction between these tasks is presented. Data parallel model is built by decomposing the tasks among the processors. The implementation and testing of the parallel model have conducted using SESE academic simulator under Fedora 11 version at Linux OS environment.