# An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction

arXiv: https://arxiv.org/abs/2412.02211 · HTML: https://mlsanity.com/paper/2412.02211/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 3 Dec 2024.

## Builds on (most cited first, 11 of 11)

- [Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining](https://mlsanity.com/paper/2409.14327/) (2024)
- [Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification](https://mlsanity.com/paper/2411.12151/) (2024)
- [Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentation](https://mlsanity.com/paper/2410.13099/) (2024)
- [Adaptive Cache Management for Complex Storage Systems Using CNN-LSTM-Based Spatiotemporal Prediction](https://mlsanity.com/paper/2411.12161/) (2024)
- [A Recommendation Model Utilizing Separation Embedding and Self-Attention for Feature Mining](https://mlsanity.com/paper/2410.15026/) (2024)
- [Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study](https://mlsanity.com/paper/2410.20792/) (2024)
- [Robust Graph Neural Networks for Stability Analysis in Dynamic Networks](https://mlsanity.com/paper/2411.11848/) (2024)
- [Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processing](https://mlsanity.com/paper/2409.15576/) (2024)
- [Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments](https://mlsanity.com/paper/2411.05346/) (2024)
- [A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation](https://mlsanity.com/paper/2411.12157/) (2024)
- [Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Management](https://mlsanity.com/paper/2410.19211/) (2024)

## Built on by (most cited first, 9 of 9)

- [A Deep Learning Framework Integrating CNN and BiLSTM for Financial Systemic Risk Analysis and Prediction](https://mlsanity.com/paper/2502.06847/) (2025)
- [Machine Learning Techniques for Pattern Recognition in High-Dimensional Data Mining](https://mlsanity.com/paper/2412.15593/) (2024)
- [Accurate Medical Named Entity Recognition Through Specialized NLP Models](https://mlsanity.com/paper/2412.08255/) (2024)
- [Dynamic Scheduling Strategies for Resource Optimization in Computing Environments](https://mlsanity.com/paper/2412.17301/) (2024)
- [Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services](https://mlsanity.com/paper/2508.14503/) (2025)
- [A Survey on Foundation-Model-Based Industrial Defect Detection](https://mlsanity.com/paper/2502.19106/) (2025)
- [Computer Vision-Driven Gesture Recognition: Toward Natural and Intuitive Human-Computer](https://mlsanity.com/paper/2412.18321/) (2024)
- [Feature Space Topology Control via Hopkins Loss](https://mlsanity.com/paper/2509.11154/) (2025)
- [From Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworks](https://mlsanity.com/paper/2606.22258/) (2026)

## Abstract (arXiv)

This study proposes an automated data mining framework based on autoencoders and experimentally verifies its effectiveness in feature extraction and data dimensionality reduction. Through the encoding-decoding structure, the autoencoder can capture the data's potential characteristics and achieve noise reduction and anomaly detection, providing an efficient and stable solution for the data mining process. The experiment compared the performance of the autoencoder with traditional dimensionality reduction methods (such as PCA, FA, T-SNE, and UMAP). The results showed that the autoencoder performed best in terms of reconstruction error and root mean square error and could better retain data structure and enhance the generalization ability of the model. The autoencoder-based framework not only reduces manual intervention but also significantly improves the automation of data processing. In the future, with the advancement of deep learning and big data technology, the autoencoder method combined with a generative adversarial network (GAN) or graph neural network (GNN) is expected to be more widely used in the fields of complex data processing, real-time data analysis and intelligent decision-making.