<ul><li>This paper proposes a frequent pattern data mining algorithm based on support vector machine (SVM) in high-dimensional and sparse data environments.</li><li>The algorithm converts the frequent pattern mining task into a classification problem and utilizes SVM to improve accuracy and robustness.</li><li>Experimental results show that the proposed algorithm outperforms traditional models in terms of support, confidence, and lift.</li><li>Future research directions include incorporating deep learning and ensemble learning frameworks for improved scalability and adaptability.</li></ul>

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

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