Method, device, computer equipment and storage medium for determining cluster centers

Abstract

The embodiment of the present disclosure discloses a method, device, computer equipment, and storage medium for determining a cluster center. The method includes: converting each two-dimensional geographic location information in the geographic location information set to be processed by using geographic location coding technology It is one-dimensional location coding information; a dictionary tree is generated according to each location coding information, a tree node in the dictionary tree corresponds to a set geographic location area, and the geographic location area corresponding to a child node belongs to the parent node of the child node Within the corresponding geographic location area; according to the positional relationship between each tree node and each geographic location information in the dictionary tree, and the quantity value of the geographic location information associated with each tree node, calculate the corresponding The density value of ; according to the density value, at least one cluster center is determined in the geographic location information set. The technical solutions of the embodiments of the present disclosure can reduce the computational complexity of the cluster center in the clustering algorithm.

Description

technical field [0001] The embodiments of the present disclosure relate to the technical field of data processing, and in particular to a method, device, computer equipment, and storage medium for determining a cluster center. Background technique [0002] The goal of the density-based clustering algorithm is to find high-density areas separated by low-density areas. Generally speaking, it is to find out the clustered points (high density), and the points where there are few and sparse points (low density) are used as segmentation. area. [0003] The core idea of ​​the density-based clustering algorithm is to first find the points with higher density according to the obtained position data, and then gradually connect the similar high-density points into one piece, and then generate various clusters, each cluster corresponds to a cluster center. [0004] In the process of implementing the present disclosure, the inventors found that the existing density-based clustering alg...

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Problems solved by technology

[0004] In the process of implementing the present disclosure, the inventor found that the existing density-based clustering algorithm has the following defects: the location data involved in the density-based clustering algorithm is usually calculated in the form of longitude and latitude information, and the calculation complexity is relatively high

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