mlsanityData to 7 Oct 2026

Machine Learning Techniques for Pattern Recognition in High-Dimensional Data Mining

arXiv 2412.15593 ↗First posted 20 Dec 2024cs.LG cs.AIcited by 8

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Abstract (arXiv)

This paper proposes a frequent pattern data mining algorithm based on support vector machine (SVM), aiming to solve the performance bottleneck of traditional frequent pattern mining algorithms in high-dimensional and sparse data environments. By converting the frequent pattern mining task into a classification problem, the SVM model is introduced to improve the accuracy and robustness of pattern extraction. In terms of method design, the kernel function is used to map the data to a high-dimensional feature space, so as to construct the optimal classification hyperplane, realize the nonlinear separation of patterns and the accurate mining of frequent items. In the experiment, two public datasets, Retail and Mushroom, were selected to compare and analyze the proposed algorithm with traditional FP-Growth, FP-Tree, decision tree and random forest models. The experimental results show that the algorithm in this paper is significantly better than the traditional model in terms of three key indicators: support, confidence and lift, showing strong pattern recognition ability and rule extraction effect. The study shows that the SVM model has excellent performance advantages in an environment with high data sparsity and a large number of transactions, and can effectively cope with complex pattern mining tasks. At the same time, this paper also points out the potential direction of future research, including the introduction of deep learning and ensemble learning frameworks to further improve the scalability and adaptability of the algorithm. This research not only provides a new idea for frequent pattern mining, but also provides important technical support for solving pattern discovery and association rule mining problems in practical applications.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 17 of its 28 references that are in the map. First posted on arXiv 20 Dec 2024.

Builds on

17 of 28 references.

References · 17

  1. 2024Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Miningcited by 47
  2. 2024Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networkscited by 33
  3. 2024Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Trainingcited by 13
  4. 2024Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold Start Issuescited by 12
  5. 2024Comprehensive Evaluation of Multimodal AI Models in Medical Imaging Diagnosis: From Data Augmentation to Preference-Based Comparisoncited by 10
  6. 2024Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphscited by 9
  7. 2024Optimizing Gesture Recognition for Seamless UI Interaction Using Convolutional Neural Networkscited by 9
  8. 2024Enhancing Few-Shot Learning with Integrated Data and GAN Model Approachescited by 9
  9. 2024An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reductioncited by 9
  10. 2024Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systemscited by 9
  11. 2024Robust Graph Neural Networks for Stability Analysis in Dynamic Networkscited by 8
  12. 2024Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Datacited by 7
5 more
  1. 2024Accurate Medical Named Entity Recognition Through Specialized NLP Modelscited by 7
  2. 2024Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Modelscited by 6
  3. 2024Enhancing Recommendation Systems with GNNs and Addressing Over-Smoothingcited by 5
  4. 2024Optimizing Multi-Task Learning for Enhanced Performance in Large Language Modelscited by 5
  5. 2024Stock Type Prediction Model Based on Hierarchical Graph Neural Networkcited by 5

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