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<p>Along with the rapid growth of multimedia data, existing deep clustering methods often rely on autoencoders (AE) to extract feature embeddings for subsequent clustering. However, these methods tend to capture only the predominant characteristics of each sample, ignoring potential inter-sample relationships and true assignments. To address this limitation, we propose a enhancing deep fuzzy K-means clustering with pseudo-category features (DFKMP). Our method employs a fully connected network, P-Net, to extract pseudo-category features from the data and constructs a pseudo-graph to depict sample correlations. To avoid the impact of non-transitivity in the pseudo-graph, we introduce a pseudo-label supervised feature learning process. This enables DFKMP to obtain suitable feature embeddings for fuzzy clustering. Experimental results on six benchmark datasets demonstrate that DFKMP significantly outperforms prevailing fuzzy clustering techniques across different evaluation metrics, achieving notable gains in accuracy, purity, and normalized mutual information. Our work highlights the potential of integrating pseudo-category features into deep fuzzy clustering to enhance performance and capture complex data relationships. Codes are available <a href="https://github.com/hlf-art/DFKMP">https://github.com/hlf-art/DFKMP</a>.</p>

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Enhancing deep fuzzy K-means clustering with pseudo-category features: a novel approach

  • 期刊论文

  • 发表日期：

    2026年1月13日

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  • Xiaodong Wang,
  • Longfu Hong,
  • Fei Yan,
  • Hongmin Hu,
  • Zhiqiang Zeng,
  • Pengtao Wu,
  • Haiyan Huang,
  • Hangqi Zhang

摘要

Along with the rapid growth of multimedia data, existing deep clustering methods often rely on autoencoders (AE) to extract feature embeddings for subsequent clustering. However, these methods tend to capture only the predominant characteristics of each sample, ignoring potential inter-sample relationships and true assignments. To address this limitation, we propose a enhancing deep fuzzy K-means clustering with pseudo-category features (DFKMP). Our method employs a fully connected network, P-Net, to extract pseudo-category features from the data and constructs a pseudo-graph to depict sample correlations. To avoid the impact of non-transitivity in the pseudo-graph, we introduce a pseudo-label supervised feature learning process. This enables DFKMP to obtain suitable feature embeddings for fuzzy clustering. Experimental results on six benchmark datasets demonstrate that DFKMP significantly outperforms prevailing fuzzy clustering techniques across different evaluation metrics, achieving notable gains in accuracy, purity, and normalized mutual information. Our work highlights the potential of integrating pseudo-category features into deep fuzzy clustering to enhance performance and capture complex data relationships. Codes are available https://github.com/hlf-art/DFKMP.