Grid-based spatial multi-scale fast clustering method

Abstract

The invention discloses a grid-based spatial multi-scale fast clustering method. The method includes the following steps that: S1, a data scale is selected, the size of grids is determined, gridding is performed on sample data, and the density values of the grids is put into statistics; S2, an initial density threshold is specified, all grids satisfying the threshold condition are reserved, and apreliminary density matrix is obtained; S3, a filter template is specified according to an observation scale, and convolution operation is performed on a global grid space; S4, a connected region is generated through neighborhood search so as to be adopted as a preliminary clustering result, integration operation is performed on the grids, the grid space is mapped onto an original point set, and an original point set clustering result is obtained; S5, the observation scale is adjusted, a transformed new filter is adopted to perform operation in the S3 and S4 on a result matrix again, a clustering result of the next observation scale is obtained; and S6, the data scale is changed, the S1 to S5 are repeated, clustering results under different data scales are obtained. The algorithm of the invention has the advantages of low complexity, high clustering efficiency and high precision, and can meet the requirements of real-time multi-scale clustering and visual analysis of massive point sets.

Description

technical field [0001] The invention relates to the field of big data analysis, mining and visualization, in particular to a grid-based spatial multi-scale rapid clustering method. Background technique [0002] Clustering is an important means of exploratory data analysis and has a wide range of applications. However, the traditional clustering methods did not clearly propose the concept of data scale and observation scale and explain their role in cluster analysis, and rarely explicitly use these two scale factors as the basis for clustering, which limits our Observing and analyzing the dimensions of things makes the clustering results not objective and comprehensive. Especially in the scenario of massive spatio-temporal data, the information contained in the data is usually more complex and richer in structure, and it is difficult for a single-scale clustering algorithm to fully mine the pattern rules. In view of the one-sided cognition problem caused by the single scale...

AI Technical Summary

Problems solved by technology

[0003] The technical problem to be solved by the present invention is to provide a grid-based Spatial multi-scale fast clustering method

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