[Verse 1] When data's got no labels to guide the way No teacher telling right from wrong today We dive into the patterns hiding deep inside Unsupervised learning is our faithful guide K-means draws circles, finds the center point Groups similar data at each cluster joint Initialize centroids, then watch them move Until convergence finds the perfect groove [Chorus] Cluster, reduce, detect what's strange Find the hidden patterns that the data can arrange DBSCAN for the noise and density's call PCA reduces dimensions, standing tall Unsupervised learning, let the data speak Find the structure that we seek [Verse 2] DBSCAN doesn't need to know the count Density-based clusters, epsilon amounts Core points, border points, and outliers too Hierarchical dendrograms show us what to do Agglomerative builds from bottom up Divisive splits the data, fills the cup Gaussian mixtures with the EM way Expectation maximization saves the day [Chorus] Cluster, reduce, detect what's strange Find the hidden patterns that the data can arrange DBSCAN for the noise and density's call PCA reduces dimensions, standing tall Unsupervised learning, let the data speak Find the structure that we seek [Verse 3] When dimensions overwhelm and curse us all Principal components answer reduction's call Eigenvalues dancing, variance explained T-SNE preserves the neighbors, locally maintained UMAP keeps both local and global too Factor analysis finds the latent view Manifold learning in a lower space Visualization gives insights their place [Bridge] But what about the outliers standing alone? Isolation forests leave them on their own Random splits until they're isolated fast One-class SVM draws boundaries that last Autoencoders learn to reconstruct Anomalies fail, their errors self-destruct [Chorus] Cluster, reduce, detect what's strange Find the hidden patterns that the data can arrange DBSCAN for the noise and density's call PCA reduces dimensions, standing tall Unsupervised learning, let the data speak Find the structure that we seek [Outro] No labels needed when the patterns shine Unsupervised methods work by design From clustering groups to dimensions few Anomalies detected, insights breaking through
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