Method and system for possibly fuzzy K-harmonic means clustering

Abstract

The invention provides a method and system for possibly fuzzy K-harmonic means clustering. The method includes the following steps: determining an initial clustering center; setting a parameter value of a clustering algorithm; calculating covariance of sample data; calculating a fuzzy membership value of the possibly fuzzy K-harmonic means clustering; calculating a typical value of the possibly fuzzy K-harmonic means clustering; calculating a clustering center value of the possibly fuzzy K-harmonic means clustering; judging whether an iteration termination condition is met, if on yes judgment, terminating iteration, if on no judgment, performing iteration calculation continuously; and utilizing the fuzzy membership value and the typical value to achieve division of data sets finally. The method and system for the possibly fuzzy K-harmonic means clustering effectively processes data containing noise and can obtain the fuzzy membership value and the typical value. The typical value does not belong to the fuzzy membership value and does not have probability constraint conditions, therefore the method and system for the possibly fuzzy K-harmonic means clustering is insensitive to noise, high in clustering accuracy and rapid in clustering speed.

Description

technical field [0001] The invention relates to the technical field of cluster analysis, in particular to a possible fuzzy K-harmonic mean clustering method and system. Background technique [0002] Clustering is an unsupervised learning method, which looks for the mutual connection between data and classifies according to the principle of similarity, that is, "like flock together". Clustering is widely used in pattern classification, text information extraction, image segmentation and data mining and other fields. K-means clustering is one of the famous clustering methods. Given a data set and the number of clusters k, K-means clustering can divide the data set concisely and effectively. However, the clustering results of K-means clustering depend on the selection of initial clustering centers, and different initial clustering centers will lead to different clustering results. Therefore, K-means clustering is sensitive to initial clustering centers and leads to clustering ...

AI Technical Summary

Problems solved by technology

[0006] The object of the present invention is to provide a possible fuzzy K-harmonic means clustering method and system, thereby solving the K-harmonic mean clustering noise sensitivity problem

Images