This scholarly Python project scrutinizes the efficacy of dimensionality reduction techniques—PCA, TSNE, UMAP—combined with K-means clustering on Pubmed and Web Content datasets. The initiative evaluates these methodologies' capacity to maintain the intrinsic data structure post-reduction, offering critical insights into their applicability in complex data analysis scenarios.

• Data Exploration: Detailed examination and preprocessing of datasets to ensure quality and relevance.
• Methodological Application: Implementation of PCA, TSNE, and UMAP to compress and visualize data dimensions.
• Clustering Analysis: Utilization of K-means to cluster reduced datasets, evaluating cluster coherence and separation.
• Performance Evaluation: Analysis of dimensionality reduction and clustering results using metrics like silhouette scores.

1. PubMed 20k RCT: Clinical research article abstracts categorized into stages of clinical study.
2. Web Content: Varied web text data spanning 16 categories, from education to e-commerce.

• Dimensionality Reduction: Application and critical analysis of PCA, TSNE, and UMAP for insightful data compression.
• Clustering Quality: Integration with K-means clustering to assess alterations in data cluster fidelity.
• Hyperparameter Optimization: Examination of parameter impacts on method performance, emphasizing perplexity (TSNE) and neighbor count (UMAP).

• Method Efficacy: Diverse strengths observed across PCA (linear relationships), TSNE, and UMAP (complex local structures).
• Optimal Parameterization: Demonstrates the significance of hyperparameter fine-tuning in enhancing lower-dimensional data representations.
• Quantitative Metrics: Silhouette scores and agreement coefficients facilitated a comparative analysis, showcasing UMAP's superior balance in structure preservation.