POFCM: A Parallel Fuzzy Clustering Algorithm for Large Datasets

Clustering algorithms have proven to be a useful tool extract knowledge and support decision making by processing large volumes of data. Hard fuzzy clustering been used successfully identify patterns trends in many areas, such as finance, healthcare, marketing. However, these significantly increase their solution time the size datasets solved increase, use unfeasible. In this sense, parallel has an efficient alternative reduce time. It established that implementation requires its redesign optimise hardware resources platform will used. article, we propose new Hybrid OK-Means Fuzzy C-Means (HOFCM) algorithm, which is variant C-Means, OpenMP. An advantage using OpenMP scalability. The efficiency compared against HOFCM algorithm. experimental results real synthetic show our tends more efficiently solve instances with number clusters dimensions. Additionally, shows excellent concerning speedup metrics. Our main contribution algorithm for scalable not limited specific domain.