A Comprehensive Review of AI-Enhanced Unsupervised Classification Frameworks Utilizing CART Algorithms
Keywords:
AI-enhanced CART, Unsupervised learning, Clustering trees, Hybrid algorithms, Explainable AIAbstract
This review examines the evolution and advancements of AI-enhanced unsupervised classification frameworks built upon Classification and Regression Trees (CART), highlighting their growing relevance in handling complex, high-dimensional, and unlabeled datasets. While traditional CART has been predominantly applied in supervised contexts, recent innovations integrate deep learning–based feature extraction, reinforcement learning for adaptive split selection, and hybrid clustering-tree architectures to enhance performance, scalability, and interpretability. The review synthesizes contemporary research from 2018 onward, offering a comprehensive analysis of theoretical foundations, algorithmic extensions, and major application areas including healthcare analytics, cybersecurity, remote sensing, and market segmentation. Additionally, it identifies key limitations such as computational overhead, sensitivity to data sparsity, and challenges in optimizing tree structures for unsupervised tasks. The study concludes by outlining future research opportunities aimed at improving transparency, robustness, and integration with semi-supervised and explainable AI frameworks.
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