Deep multi-semantic fuzzy K-means with adaptive weight adjustment

Existing deep fuzzy clustering methods employ deep neural networks to extract high-level feature embeddings from data, thereby enhancing subsequent clustering and achieving superior performance compared to traditional methods. However, solely relying on feature embeddings may cause clustering models to ignore detailed information within data. To address this issue, this paper designs a deep multi-semantic fuzzy K-means (DMFKM) model. Our method harnesses the semantic complementarity of various kinds of features within autoencoder to improve clustering performance. Additionally, to fully exploit the contribution of different types of features to each cluster, we propose an adaptive weight adjustment mechanism to dynamically calculate the importance of different features during clustering. To validate the effectiveness of the proposed method, we applied it to six benchmark datasets. DMFKM significantly outperforms the prevailing fuzzy clustering techniques across different evaluation metrics. Specifically, on the six benchmark datasets, our method achieves notable gains over the second-best comparison method, with an ACC improvement of approximately 2.42%, a Purity boost of around 1.94%, and an NMI enhancement of roughly 0.65%.