Fuzzy clustering evaluation method based on dichotomy modularity

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. 本发明提出了一种基于二分模块度的模糊聚类评价方法，将类内紧致性、类间分离性与二分模块度融合在一起，用于确定模糊C均值聚类算法的最优分类结果。该指标结合类内紧致性与类间分离性，增强了指标的鲁棒性，且能够准确检测最佳类簇数目，提高了评估聚类结果的准确率。