Improved K-means clustering algorithm based on density radius

Abstract

The invention, which relates to the field of clustering algorithms, discloses an improved K-means clustering algorithm based on density radius so that problems that a local optimal solution exists, the sensitivity to the noises and outliers is high, and the k value selection is not accurate of the existing K-means clustering algorithm can be solved. All sample points are ranked according to the density radius, the sample point with the largest density radius is used as an initial value, the above-mentioned steps are repeated, all the initial points and the category number k are selected, and clustering operation is stated; two centroids at nearest distances are selected among the clustered category centroids, the categories of the two centroids are taken separately and viewed as a dichotomy, a Bayesian score of the dichotomy is calculated, the two categories are combined into one, a Bayesian score after combination is calculated, whether the two categories need to be combined is determined based on the score, and the above-mentioned steps are repeated until no combination is needed. The clustering algorithm is suitable for big data clustering processing.

Description

technical field [0001] The invention relates to the field of clustering algorithms, in particular to an improved K-means clustering algorithm based on density radius. Background technique [0002] Clustering is to divide some physical or abstract objects into several clusters according to the similarity between objects, so that the data in the same cluster has a high similarity, and the data in different clusters are similar Sex is low. Clustering is an unsupervised learning method that classifies unlabeled data without prior information. The K-means algorithm is the most commonly used typical partitioning algorithm in cluster analysis. This algorithm divides the data according to a certain similarity measurement method, so that the distance between each data and the centroid of the cluster to which it belongs is as small as possible. , the algorithm is widely used because of its simplicity and high efficiency. But at the same time, it also has some defects, such as the n...

AI Technical Summary

Problems solved by technology

[0003] The technical problem to be solved in the present invention is: provide a kind of improved K-means clustering algorithm based on density radius, solve the local optimum solution that existing K-means clustering algorithm exists, be sensitive to noise and outlier point, k Inaccurate value selection problem

Images