The exponential growth of high dimensional data in healthcare monitoring systems, intensive care repositories, and task oriented dialog systems has intensified the need for robust unsupervised learning frameworks capable of discovering intrinsic structure without heavy reliance on labeled data. Density based clustering algorithms such as DBSCAN and its variants have remained central to this effort due to their capacity to detect arbitrarily shaped clusters and manage noise effectively. However, persistent challenges remain in parameter estimation, validation, scalability, and performance under varying density distributions. Simultaneously, advances in pretrained transformer models and sentence embedding architectures have significantly transformed natural language processing tasks including entity linking and premise selection. The convergence of transformer driven semantic embeddings with density based clustering presents a promising research direction for healthcare analytics and dialog intelligence.

This study develops a comprehensive theoretical and methodological framework that integrates pretrained transformer embeddings, dimensionality reduction methods such as UMAP and t SNE, and optimized density based clustering strategies including adaptive and stratified parameter estimation techniques. The work draws from foundational research in DBSCAN and GDBSCAN, parameter optimization methods using differential evolution and multi verse optimization, adaptive density algorithms, internal validation metrics such as silhouette and Davies Bouldin indices, and large scale healthcare datasets including MIMIC II and MIMIC Extract. The theoretical exposition also incorporates developments in Sentence BERT, sentence MPNet representations, and transformer based entity linking for task oriented dialog systems.

The proposed framework addresses four interrelated challenges: high dimensional embedding instability, density heterogeneity in clinical datasets, automatic parameter selection in DBSCAN family algorithms, and validation interpretability in unsupervised contexts. Through descriptive experimental analysis on intensive care waveform data, clinical phenotype clustering, and semantic dialog entity linking embeddings, we demonstrate that optimized density based clustering combined with manifold preserving dimensionality reduction enhances cluster stability, interpretability, and robustness to noise. Stratified epsilon estimation and grid based minimum sample tuning significantly reduce parameter sensitivity compared to classical heuristic approaches.

The results indicate that transformer derived embeddings clustered via optimized DBSCAN variants outperform centroid based clustering approaches in preserving semantic coherence and clinical phenotype separation. Furthermore, density based clustering proves particularly effective in identifying rare but clinically significant outliers such as early sepsis patterns in intensive care monitoring data. Internal validation metrics, when interpreted jointly rather than in isolation, provide nuanced insights into cluster compactness and separation.

This research contributes a unified conceptual architecture for combining modern language models with density based unsupervised learning in healthcare and dialog systems. The framework offers theoretical clarity, methodological rigor, and practical guidance for researchers and practitioners working with large scale, noisy, and high dimensional datasets.