Implementation Parallel Computation for Automatic Clustering - Bayu Dwiyan Satria; Ali Ridho Barakbah; Amang Sudarsono (Institute of Computer Science (IOCS), 2021)
Developments in the industrial world, especially in the field of computer technology, demand solutions to needs ranging from computing resources, storage media, and communication speeds. This was followed by many new studies in this field, including those related to clustering. Clustering is an exploratory data analysis tool that deals with the task of grouping objects that are similar to each other [1], [2]. Clustering requires computers with high resources, usually the price for computers with high resources, while computers with not too high specifications will be less reliable in handling such large data. Clustering that runs on a single-core processor takes a long time to execute tasks, so parallel computing is needed to speed up computing performance especially at Automatic Clustering. This research will produce faster performance in grouping large data by utilizing parallel computing and automatic clustering methods as methods for grouping data. This technology allows data processing to be carried out in parallel and distributed in hundreds or even thousands of computers, so this technology is very appropriate for processing very large amounts of data)