Unsupervised Learning Algorithms

Unsupervised learning algorithms find hidden patterns and structures in unlabeled data. Unlike supervised learning, these algorithms work without predefined outputs. Here are key unsupervised learning algorithms:

K-means Clustering

Partitions data into K distinct clusters based on feature similarity. Each data point belongs to the cluster with the nearest mean. Widely used for customer segmentation and image compression.

Hierarchical Clustering

Creates a tree of clusters by either merging smaller clusters (agglomerative) or splitting larger ones (divisive). Useful when you want to explore different levels of grouping.

Principal Component Analysis (PCA)

Reduces data dimensionality by transforming high-dimensional data into lower dimensions while preserving maximum variance. Essential for data visualization and feature extraction.

t-SNE (t-Distributed Stochastic Neighbor Embedding)

A dimensionality reduction technique that's particularly good at visualizing high-dimensional data in 2D or 3D space. Preserves local structure better than PCA.

DBSCAN (Density-Based Spatial Clustering)

Groups together points that are closely packed while marking points in low-density regions as outliers. Effective for finding clusters of arbitrary shape.

Autoencoders

Neural networks that learn compressed representations of data by trying to reconstruct their input. Useful for dimensionality reduction and anomaly detection.

Association Rule Learning

Discovers interesting relationships between variables in large datasets. Common in market basket analysis to find items frequently bought together.

Gaussian Mixture Models (GMM)

Models data as a mixture of several Gaussian distributions. More flexible than K-means as it allows for clusters of different sizes and shapes.

These algorithms are essential tools in exploratory data analysis, pattern recognition, and feature learning, helping to uncover the underlying structure of complex datasets.