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Concepts\n\n### Clustering\n- **K-Means**: Centroid-based, assumes spherical clusters\n- **Hierarchical**: Dendrogram-based, any distance\n- **DBSCAN**: Density-based, finds arbitrary shapes\n- **GMM**: Probabilistic, soft clustering\n\n### Dimensionality Reduction\n- **PCA**: Linear, variance-based\n- **t-SNE**: Non-linear, visualization\n- **UMAP**: Non-linear, structure preservation\n- **Autoencoders**: Non-linear, learned\n\n### Evaluation\n- Silhouette score\n- Davies-Bouldin index\n- Calinski-Harabasz index\n- Reconstruction error (for autoencoders)\n","## What I do\n\n- Discover hidden patterns in data\n- Group similar observations without labels\n- Reduce feature dimensions\n- Find outliers and anomalies\n- Learn latent representations\n- Generate synthetic data\n\n## When to use me\n\nUse me when:\n- Labels are unavailable or expensive\n- Exploring unknown data patterns\n- Reducing data complexity\n- Preprocessing for supervised learning\n- Real-time clustering needed\n\n## Key Concepts\n\n### Clustering\n- **K-Means**: Centroid-based, assumes spherical clusters\n- **Hierarchical**: Dendrogram-based, any distance\n- **DBSCAN**: Density-based, finds arbitrary shapes\n- **GMM**: Probabilistic, soft clustering\n\n### Dimensionality Reduction\n- **PCA**: Linear, variance-based\n- **t-SNE**: Non-linear, visualization\n- **UMAP**: Non-linear, structure preservation\n- **Autoencoders**: Non-linear, learned\n\n### Evaluation\n- Silhouette score\n- Davies-Bouldin index\n- Calinski-Harabasz index\n- Reconstruction error (for autoencoders)\n",{"name":5,"license":15,"metadata":31,"description":7,"compatibility":21},{"audience":32,"category":33},"machine-learning-engineers","artificial-intelligence","main","2ea764e153842704d24a2e9aa4170441a7f03081",".opencode\u002Fskills\u002Fvisualization\u002Funsupervised","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito","https:\u002F\u002Fgithub.com\u002FNeuralBlitz","nuralnexus.org","Independent Multidisciplinary 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