In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional data mining methods are inadequate when faced with large-scale, high-dimensional and complex data. Especially when labeled data is scarce, their performance is greatly limited. This study optimizes data mining algorithms by introducing semi-supervised learning methods, aiming to improve the algorithm's ability to utilize unlabeled data, thereby achieving more accurate data analysis and pattern recognition under limited labeled data conditions. Specifically, we adopt a self-training method and combine it with a convolutional neural network (CNN) for image feature extraction and classification, and continuously improve the model prediction performance through an iterative process. The experimental results demonstrate that the proposed method significantly outperforms traditional machine learning techniques such as Support Vector Machine (SVM), XGBoost, and Multi-Layer Perceptron (MLP) on the CIFAR-10 image classification dataset. Notable improvements were observed in key performance metrics, including accuracy, recall, and F1 score. Furthermore, the robustness and noise-resistance capabilities of the semi-supervised CNN model were validated through experiments under varying noise levels, confirming its practical applicability in real-world scenarios.

本研究解决了在有限标记数据下图像分类中传统数据挖掘方法效果不佳的问题。通过引入半监督学习方法，尤其是自我训练的方法与卷积神经网络的结合，显著提升了模型对未标记数据的利用能力和预测性能。实验结果显示，该方法在CIFAR-10图像分类数据集上的表现明显优于传统机器学习技术，并展现出良好的鲁棒性和抗噪声能力，具有实际应用潜力。

利用半监督学习增强有限标记数据下的图像分类数据挖掘