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MR-DBIFOA: a parallel Density-based Clustering Algorithm by Using Improve Fruit Fly Optimization
2022
Diànnǎo xuékān
<p>Clustering is an important technique for data analysis and knowledge discovery. In the context of big data, the density-based clustering algorithm faces three challenging problems: unreasonable division of data gridding, poor parameter optimization ability and low efficiency of parallelization. In this study, a density-based clustering algorithm by using improve fruit fly optimization based on MapReduce (MR-DBIFOA) is proposed to tackle these three problems. Firstly, based on KD-Tree, a
doi:10.53106/199115992022023301010
fatcat:gzehehxvwngipg3vhdeeiuuk4u