Fuzzy clustering evaluation method based on dichotomy modularity

Abstract

The invention provides a fuzzy clustering evaluation method based on dichotomy modularity, which integrates intra-class compactness, inter-class separability and dichotomy modularity together and is used for determining an optimal classification result of a fuzzy C-means clustering algorithm. The index is combined with intra-class compactness and inter-class separability, the robustness of the index is enhanced, the optimal cluster number can be accurately detected, and the accuracy of evaluating the clustering result is improved.

Description

technical field [0001] The invention relates to a clustering evaluation method, in particular to a binary modularity-based fuzzy clustering evaluation method, which belongs to the field of data mining. Background technique [0002] As one of the key technologies of data mining, clustering can divide a set of samples into multiple clusters, so that the similarity between elements in the same cluster is as high as possible, while the similarity between elements in different clusters is as low as possible. [0003] Fuzzy clustering represented by the FCM (Fuzzy C Means) algorithm fuzzifies the value of the membership degree, allowing a sample to belong to multiple clusters with different probabilities, which is more in line with people's cognition of the distribution of samples, so fuzzy clustering Research is timeless. So far, a large number of fuzzy clustering algorithms have made continuous progress in accuracy, efficiency, robustness, etc., which have effectively promoted ...

AI Technical Summary

Problems solved by technology

Some widely used effectiveness indicators such as PC, PE, and MPC are too dependent on the membership degree generated by the FCM algorithm, and because the FCM algorithm itself is sensitive to noise points and outliers, the effectiveness indicators aimed at enhancing robustness have been proposed one after another, but the accuracy still needs to be improved