Cluster-like center determination method, device, computer device and storage medium

Abstract

The disclosed embodiment discloses a method, a device, a computer device and a storage medium for determining a cluster-like center. The method comprises the following steps: adopting a geographic position coding technique, converting each two-dimensional geographic position information in a geographic position information set to be processed into one-dimensional geographic position coding information; A dictionary tree is generated according to the location coding information. A tree node in the dictionary tree corresponds to a set geographic location area, and a geographic location area corresponding to a child node belongs to a geographic location area range corresponding to a parent node of the child node. Calculating a density value corresponding to each geographic position information according to a positional relationship between each tree node and each geographic position information in the dictionary tree and a quantity value of geographic position information associated witheach tree node; At least one cluster center is determine in that geographic location information set based on the density value. The technical proposal of the disclosed embodiment 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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