Clustering cluster calculation method and device, terminal and storage medium

Abstract

The embodiment of the invention discloses a clustering cluster calculation method and device, a terminal and a storage medium. The method comprises the following steps: acquiring target feature data of communication equipment contained in a sample equipment set at a preset time magnitude within a preset duration, and constructing a standard initial matrix according to the target feature data; extracting a preset number of communication equipment samples from the sample equipment set, and constructing a clustering center matrix according to the target feature data corresponding to the preset number of communication equipment samples; calculating a target distance value between each row of elements in the clustering center matrix and all rows of elements in the standard initial matrix, and constructing a distance vector; and calculating the number of class clusters contained in the target feature data by adopting a parameter-window-free function probability density estimation method based on the distance vector. In addition, the embodiment of the invention further discloses a multi-dimensional time series clustering cluster calculation device, a terminal and a computer readable medium. According to the invention, the method can achieve the precise obtaining of the number of multi-dimensional class clusters.

Description

technical field [0001] The present invention relates to the technical field of information processing, in particular to a cluster computing method, device, terminal and storage medium. Background technique [0002] Cluster analysis refers to the analytical process of grouping a collection of physical or abstract objects into multiple classes consisting of similar objects. Among them, a core problem in cluster analysis is that the number of clusters in the data must be known in advance, because many clustering algorithms need to input the number of clusters (number of clusters) as a parameter to run the algorithm. However, the number of clusters present in real data is usually unknown. In practical applications, the existing methods for determining the optimal number of clusters are mainly researched on numerical attribute data, and the data distribution structure is usually convex blob data. Specifically combining the clustering algorithm and the internal validity index, a...

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Problems solved by technology

[0003] In view of this, the present invention provides a cluster calculation method, device, terminal and storage medium, which are used to solve the problem in the prior art that the accuracy of the number of clusters obtained based on user experience is low

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