基于改进的K-means聚类分区均匀化空间学习索引OA北大核心CSTPCD

With the rapid increase of data size,the defects of traditional spatial indexing become more and more apparent.In comparison,learning indexing is based on data distribution.Its volume will not expand with the increase of the amount of data,and can achieve better performance without performing hierarchical comparison.Nevertheless,there are still two difficulties in applying the idea of learning indexing to spatial data:(1)How to choose appropriate dimension reduction method to sort the spatial data.(2)How to simplify data distribution of the dimension reduced data and make it easy to fit.This paper proposes a new type of grid mixed cluster partition learning indexing(grid-ml)based on the idea of learning indexing.In view of the above two difficulties,grid-ml uses z curve to reduce the dimension,and deals with the jumping problem with double-layer grid structure.Then,the improved K-means clustering method is used to simplify data distribution.The results show that grid-ml builds fast with small spatial storage volume,and can query fast as well,demonstrating significant advantages over the traditional spatial indexing approach.