Parallelized Rough K-means Clustering with Erlang Programming

—Currently multi-core processors have been available on most personal computers. To get the maximum benefit of computational power from the multi-core architecture, we need a new design on existing algorithms and software. We propose the parallelization of the rough k-means clustering algorithm. In the rough k-means clustering algorithm, each cluster has been formed regarding the two approximations, a lower and an upper approximation. To make the rough k-means clustering be better parallelized, we employ Erlang as a language for concurrent programming. Sending and receiving messages between a master and the concurrently created process of the Erlang language are done in an asynchronous manner. Therefore, the implementation can be highly parallel and fault tolerant. The experimental results demonstrate considerable speedup rate of the proposed parallel rough k-means clustering method, compared to the serial rough k-means approach.