Model Architectures10 min read• Published July 24, 2026

SVMs, Clustering (K-Means/DBSCAN), Dimensionality Reduction (PCA/t-SNE/UMAP) & Time Series

Margin maximization, kernel tricks, unsupervised clustering, manifold visualization, and temporal autoregression.

SK
Sri Kanish P
Co-Founder & ROS Developer

⚡ Executive Summary

Explore SVM maximum margin boundaries, unsupervised clustering (K-Means, DBSCAN), dimensionality reduction (PCA, t-SNE, UMAP), and time series models.

Key Takeaways

  • ✓Support Vector Machines (SVM) — covered in depth with practical examples, formulas, and code.
  • ✓Unsupervised Clustering — covered in depth with practical examples, formulas, and code.
  • ✓Dimensionality Reduction — covered in depth with practical examples, formulas, and code.

SVMs, Unsupervised Learning & Time Series Reference

#1. Support Vector Machines (SVM)

Finds the optimal decision boundary (hyperplane) maximizing margin distance between classes. Kernel trick (RBF, Polynomial) projects data into higher dimensions for non-linear boundaries.

#2. Unsupervised Clustering

  • K-Means: Partitions data into K clusters minimizing within-cluster variance. Sensitive to centroid initialization (K-Means++).
  • DBSCAN: Density-based clustering finding arbitrary shaped clusters without specifying K; identifies noise points.
  • Hierarchical Clustering: Agglomerative bottom-up tree merging.

    • #3. Dimensionality Reduction

  • PCA (Principal Component Analysis): Linear orthogonal projection maximizing feature variance.
  • t-SNE & UMAP: Non-linear manifold learning preserving local neighborhood structures for high-dimensional data visualization.

    • #4. Time Series Models

  • ARIMA (AutoRegressive Integrated Moving Average): Classical linear modeling capturing autoregression (p), differencing (d), and moving average (q).
  • Prophet: Additive model decomposing trend, seasonality, and holiday effects.
    • Tags:#SVM#K-Means#DBSCAN#PCA#t-SNE#UMAP#Time Series
    ABOUT THE AUTHOR
    SK

    Sri Kanish P

    Co-Founder & ROS Developer

    Sri Kanish P is part of the Junglans Solutions engineering team, specializing in model architectures. Junglans builds a 20-product ecosystem of local-first enterprise software — AI developer tools, encrypted communication, and data infrastructure with zero cloud telemetry.

    Meet the full Junglans engineering team ↗
    RELATED RESOURCES & REFERENCES

    This article is part of the Junglans Research knowledge base, produced alongside the engineering teams that build our production AI tools. Explore related product documentation and research:

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