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Combining Semi-supervised Clustering and Classification Under a Generalized Framework

Author

Listed:
  • Zhen Jiang

    (Jiangsu University
    Jiangsu Province Big Data Ubiquitous Perception and Intelligent Agricultural Application Engineering Research Center)

  • Lingyun Zhao

    (Jiangsu University)

  • Yu Lu

    (Jiangsu University)

Abstract

Most machine learning algorithms rely on having a sufficient amount of labeled data to train a reliable classifier. However, labeling data is often costly and time-consuming, while unlabeled data can be readily accessible. Therefore, learning from both labeled and unlabeled data has become a hot topic of interest. Inspired by the co-training algorithm, we present a learning framework called CSCC, which combines semi-supervised clustering and classification to learn from both labeled and unlabeled data. Unlike existing co-training style methods that construct diverse classifiers to learn from each other, CSCC leverages the diversity between semi-supervised clustering and classification models to achieve mutual enhancement. Existing classification algorithms can be easily adapted to CSCC, allowing them to generalize from a few labeled data. Especially, in order to bridge the gap between class information and clustering, we propose a semi-supervised hierarchical clustering algorithm that utilizes labeled data to guide the process of cluster-splitting. Within the CSCC framework, we introduce two loss functions to supervise the iterative updating of the semi-supervised clustering and classification models, respectively. Extensive experiments conducted on a variety of benchmark datasets validate the superiority of CSCC over other state-of-the-art methods.

Suggested Citation

  • Zhen Jiang & Lingyun Zhao & Yu Lu, 2025. "Combining Semi-supervised Clustering and Classification Under a Generalized Framework," Journal of Classification, Springer;The Classification Society, vol. 42(1), pages 181-204, March.
  • Handle: RePEc:spr:jclass:v:42:y:2025:i:1:d:10.1007_s00357-024-09489-9
    DOI: 10.1007/s00357-024-09489-9
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