Implementation of parallel clustering algorithms using Join and Fork model

Join and Fork model has been shown to be a powerful approach for boosting a system performance. The programming language Java supports the multithreading programming as part of the language itself instead of treating threads through the operating system. We tested several clustering algorithms implemented with Java language using multithreading approach and Join Fork approach on multi-core CPU. We try to exploit computational power from the multicore processors. Performance is increased on single core and multiple cores CPU in different ways in complexity of the algorithm and the platform. In order to utilize the intrinsic capabilities of a multi-core processor the software application must be able to execute tasks in parallel using all available CPUs. This paper analyzes about the performance of Modified Parallel K Means algorithm and Parallel Genetic K Means algorithm using Java Join and Fork Method. Fork/join method overcomes deficiencies of multithreaded execution.