# Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining

arXiv: https://arxiv.org/abs/2504.04032 · HTML: https://mlsanity.com/paper/2504.04032/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 5 Apr 2025.

## Builds on (most cited first, 10 of 10)

- [Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining](https://mlsanity.com/paper/2409.14327/) (2024)
- [Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Data](https://mlsanity.com/paper/2502.03803/) (2025)
- [Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applications](https://mlsanity.com/paper/2412.18222/) (2024)
- [Optimized Unet with Attention Mechanism for Multi-Scale Semantic Segmentation](https://mlsanity.com/paper/2502.03813/) (2025)
- [A Deep Learning Framework Integrating CNN and BiLSTM for Financial Systemic Risk Analysis and Prediction](https://mlsanity.com/paper/2502.06847/) (2025)
- [A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning](https://mlsanity.com/paper/2502.09086/) (2025)
- [AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP](https://mlsanity.com/paper/2501.14745/) (2024)
- [A Fine-Tuning Approach for T5 Using Knowledge Graphs to Address Complex Tasks](https://mlsanity.com/paper/2502.16484/) (2025)
- [EntropyStop: Unsupervised Deep Outlier Detection with Loss Entropy](https://mlsanity.com/paper/2405.12502/) (2024)
- [Combining complex networks and data mining: why and how](https://mlsanity.com/paper/1604.08816/) (2016)

## Built on by (most cited first, 6 of 6)

- [Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks](https://mlsanity.com/paper/2505.08220/) (2025)
- [Graph-Based Spectral Decomposition for Parameter Coordination in Language Model Fine-Tuning](https://mlsanity.com/paper/2504.19583/) (2025)
- [Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions](https://mlsanity.com/paper/2504.15491/) (2025)
- [Modeling Multi-Hop Semantic Paths for Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2505.05989/) (2025)
- [Towards Robust Few-Shot Text Classification Using Transformer Architectures and Dual Loss Strategies](https://mlsanity.com/paper/2505.06145/) (2025)
- [Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learning](https://mlsanity.com/paper/2505.00299/) (2025)

## Abstract (arXiv)

Complex data mining has wide application value in many fields, especially in the feature extraction and classification tasks of unlabeled data. This paper proposes an algorithm based on self-supervised learning and verifies its effectiveness through experiments. The study found that in terms of the selection of optimizer and learning rate, the combination of AdamW optimizer and 0.002 learning rate performed best in all evaluation indicators, indicating that the adaptive optimization method can improve the performance of the model in complex data mining tasks. In addition, the ablation experiment further analyzed the contribution of each module. The results show that contrastive learning, variational modules, and data augmentation strategies play a key role in the generalization ability and robustness of the model. Through the convergence curve analysis of the loss function, the experiment verifies that the method can converge stably during the training process and effectively avoid serious overfitting. Further experimental results show that the model has strong adaptability on different data sets, can effectively extract high-quality features from unlabeled data, and improves classification accuracy. At the same time, under different data distribution conditions, the method can still maintain high detection accuracy, proving its applicability in complex data environments. This study analyzed the role of self-supervised learning methods in complex data mining through systematic experiments and verified its advantages in improving feature extraction quality, optimizing classification performance, and enhancing model stability