A survey on parallel clustering technique for big data framework - Mallik, Moksud Alam; Zulkurnain, Nurul Fariza; Jamil Ahmed, S. K.; Nizamuddin, Mohammed Khaja (CRC Press, 2021)
Clustering techniques for little data sets have built excellent develops and numerous successful clustering techniques have been designed. Nevertheless, these techniques are not giving adequate results when trading with extensive data sets. The most important problems are excessive computational difficulty and lengthy evaluating time, which is not acceptable for real time context. It is very prime to process this enormous information on time. Consequently, the investigation on clustering techniques for extensive data sets has begun to be one of the major jobs in the area of big data framework. This survey centers on a sharp investigation of various parallel clustering strategies featuring the qualities of large information. A short outline of various parallel clustering techniques which are based on Map Reduce evaluating technology, the clustering techniques based on Spark evaluating technology and the clustering techniques based on Storm evaluating technology.