关键词: 密度偏差抽样, 可变网格划分, 数据挖掘, 大规模数据集, 聚类

Abstract: As the most commonly used method of reducing large-scale datasets, simple random sampling usually causes the loss of some clusters when dealing with unevenly distributed dataset. A density biased sampling algorithm based on grid can solve these defects, but both the efficiency and effect of sampling can be affected by the granularity of grid division. To overcome the shortcoming, a density biased sampling algorithm based on variable grid division was proposed. Every dimension of original dataset was divided according to the corresponding distribution, and the structure of the constructed grid was matched with the distribution of original dataset. The experimental results show that density biased sampling based on variable grid division can achieve higher quality of sample dataset and uses less execution time of sampling compared with the density biased sampling algorithm based on fixed grid division.

Key words: density biased sampling, variable grid division, data mining, large-scale dataset, clustering