Unsupervised Learning Algorithms and Task Matching Short Notes for GATE DA: Concepts, Formulas, Worked Examples & Practice

    Unsupervised Learning Algorithms and Task Matching short notes for GATE DA: 2 study cards covering concepts, formulas, shortcuts and exam traps, plus solved practice questions.

    Quick Reference: Task to Algorithm Cheat Sheet

    Final Exam Cheat Sheet

    Quick reference for task-to-algorithm mapping.

    TaskKeywordsGo-To AlgorithmsKey Constraint
    Clustering Group, segment, partition K-Means, DBSCAN, Hierarchical, GMM Shape, known K, data type
    Dim. Reduction Compress, visualize, latent PCA, t-SNE, UMAP Linear vs non-linear, global vs local
    Association Market basket, rules Apriori, FP-Growth Dataset size, support/confidence
    Anomaly Outliers, fraud, defects Isolation Forest, LOF, One-Class SVM Dimensionality, anomaly proportion
    Density Distribution, likelihood GMM, KDE Parametric vs non-parametric

    Final Revision: Algorithm Quick-Check

    Algorithm Quick-Check

    AlgorithmTypeStrengthWeakness
    K-Means Unsupervised Clustering Fast, scalable Sensitive to outliers
    K-Medoids Unsupervised Clustering Robust to outliers Slower, expensive
    LDA Supervised Classification Maximizes separation Requires labels
    PCA Unsupervised Reduction Preserves variance Linear only
    MCMC Sampling Complex distributions Slow convergence
    DBSCAN Unsupervised Clustering Arbitrary shapes Varying densities
    Final Tip: When in doubt, ask: "Does this algorithm require labels?"

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    Unsupervised Learning Algorithms and Task Matching Short Notes for GATE DA: Concepts, Formulas, Worked Examples & Practice

    Unsupervised Learning Algorithms and Task Matching short notes for GATE DA: 2 study cards covering concepts, formulas, shortcuts and exam traps, plus solved p

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