Clustering method, system and medium for automatically confirming cluster number based on coefficient of variation

Abstract

The invention discloses a clustering method, a system and a medium for automatically confirming the number of clusters based on a coefficient of variation, wherein, the density value of each data point in a data set is calculated, the density index is calculated according to the density value, and the data point with the largest density index is selected as a first clustering center; Calculating the shortest distance between each data point and the existing clustering center, calculating the probability that each data point is selected as the clustering center according to the shortest distance, and preselecting the clustering center according to the roulette disc method; Until the set cluster centers are selected, the initial cluster centers selected are used for k-means clustering to generate a corresponding number of clusters; Calculate the average intra-cluster coefficient of variation and the minimum inter-cluster coefficient of variation, then calculate the difference between theaverage intra-cluster coefficient of variation and the minimum inter-cluster coefficient of variation, compare the difference with the set value, and if the difference is less than the set value, merge the two clusters with the minimum inter-cluster coefficient of variation; Until the difference is greater than or equal to the set value, the clustering result is output.

Description

technical field [0001] The invention relates to a clustering method, system and medium for automatically confirming the number of clusters based on the coefficient of variation. Background technique [0002] With the rapid development of information technology, many industries, such as commerce, enterprises, scientific research institutions and government departments, have accumulated massive amounts of data stored in different forms. These massive amounts of data often contain various useful information. It is difficult to obtain these information only relying on the query and retrieval mechanism of the database and statistical methods, so data mining technology is also developing rapidly. Clustering analysis technology is an important research field in data mining and has been widely used in many applications. , including pattern recognition, data analysis, image processing, and market research. [0003] Clustering analysis technology is an unsupervised learning method, i...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the deficiencies of the prior art, the present invention provides a clustering method, system and medium for automatically confirming the number of clusters based on the coefficient of variation, which solves the defects of the traditional k-means++ clustering algorithm manually setting the number of clusters and improper selection of initial centroids , using the concept of variation coefficient and density index to improve the division-based k-means++ clustering algorithm, without manually setting the number of clusters, it also ensures the accuracy of the clustering results;

Images