Hongjun Zhang1,2, Zeyu Zhang3, Yilong Ruan4, Hao Ye5,6, Peng Li1,*, Desheng Shi1
CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1409-1432, 2024, DOI:10.32604/cmc.2024.055406
- 15 October 2024
Abstract The scale and complexity of big data are growing continuously, posing severe challenges to traditional data processing methods, especially in the field of clustering analysis. To address this issue, this paper introduces a new method named Big Data Tensor Multi-Cluster Distributed Incremental Update (BDTMCDIncreUpdate), which combines distributed computing, storage technology, and incremental update techniques to provide an efficient and effective means for clustering analysis. Firstly, the original dataset is divided into multiple sub-blocks, and distributed computing resources are utilized to process the sub-blocks in parallel, enhancing efficiency. Then, initial clustering is performed on each sub-block… More >
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