Adaptive clustering method

Abstract

The invention discloses an adaptive clustering method. The method comprises the following five steps: processing spatial data and selecting characteristics; performing a spatial attribute clustering operation based on a Delaunay triangulation network; clustering by considering a non-spatial attribute; optimizing a clustering result; and visualizing the clustering result. The adaptive clustering method provided by the invention solves the problem of the traditional clustering algorithm that the clustering result is uncertain due to an uncertainty of parameter setting, has the characteristics of being capable of realizing automatic operation without prior knowledge, strong in adaptability, complete in function and so on, and can effectively improve the ability of analyzing and mining a deep geographical law by spatial clustering.

Description

Technical field [0001] The invention belongs to the technical field of spatial data mining and spatial analysis, and particularly relates to an improved adaptive clustering method. Background technique [0002] Spatial data mining, as a powerful tool for discovering useful information and knowledge from a large number of complex databases, has received widespread attention in recent years. As a main research direction of spatial data mining, spatial clustering aims to use the similarity scale to measure the degree of closeness between things, and to achieve classification. It has been widely used in many fields such as classification and grading, image classification, anomaly detection, and hot spot analysis. [0003] The existing spatial clustering methods can be roughly divided into: (1) density-based method; (2) partition-based method; (3) hierarchy-based method; (4) model-based method; (6) grid-based method Methods. [0004] Existing spatial clustering algorithms are difficult...

AI Technical Summary

Problems solved by technology

[0005] Most of the existing dual clustering considering spatial and non-spatial attributes defines the distance between targets as the value of the weighted sum after the normalization of spatial attributes and non-spatial attributes, and then uses the existing spatial clustering method to achieve clustering. Class operation, the weight of spatial attributes and non-spatial attributes in this method is difficult to determine

There are also some clustering methods developed from two aspects: spatial attribute clustering and non-spatial attribute clustering, but the existing methods are difficult to realize the clustering operation adaptively, usually need to set parameters artificially, and have no effect on the quality of the clustering results. Good judging criteria, without sufficient prior knowledge, it is difficult to determine the optimal parameter value

In addition, existing methods usually do not take into account the effects of noise and spatial obstacles

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