Unsupervised Learning Algorithms

Unsupervised learning is a type of machine learning where the algorithm is provided with data that is neither classified nor labelled, meaning the model learns from the data without explicit guidance. The goal is to uncover hidden patterns or intrinsic structures in the input data.

Here are some common unsupervised learning algorithms:

1. Clustering Algorithms:
   • K-Means Clustering:
     • Partitions the data into K clusters based on feature similarity. Each data point belongs to the cluster with the nearest mean.
   • Hierarchical Clustering:
     • Builds a hierarchy of clusters by iteratively merging or splitting clusters. It can be agglomerative (bottom-up) or divisive (top-down).
   • DBSCAN (Density-Based Spatial Clustering of Applications with Noise):
     • Groups points that are closely packed together and marks points that are in low-density regions as outliers.
   • Gaussian Mixture Models (GMM):
     • Assumes that data is generated from a mixture of several Gaussian distributions and tries to learn the parameters of these distributions.

2. Dimensionality Reduction Algorithms:
   • Principal Component Analysis (PCA):
     • Reduces the dimensionality of data by transforming it into a set of uncorrelated variables called principal components.
   • t-Distributed Stochastic Neighbour Embedding (t-SNE):
     • Useful for visualizing high-dimensional data by mapping it to a lower dimension (usually 2D or 3D), while preserving the structure of the data.
   • Autoencoders:
     • A type of neural network that learns to compress data into a lower-dimensional representation and then reconstructs it back to the original.

3. Association Rule Learning:
   • Apriori Algorithm:
     • Finds frequent item sets in transactional datasets and derives association rules that express how often items are bought together.
   • Eclat (Equivalence Class Clustering and bottom-up Lattice Traversal):
     • Another algorithm used for association rule mining but focuses on itemset intersections.

4. Anomaly Detection:
   • Isolation Forest:
     • Works by randomly partitioning data and isolating points that appear as anomalies. It’s based on the idea that anomalies are easier to isolate.
   • One-Class SVM:
     • A variant of SVM used for anomaly detection where the algorithm learns a boundary that encapsulates the normal data points, and anything outside this boundary is classified as an anomaly.

5. Generative Models:
   • Generative Adversarial Networks (GANs):
     • Consists of two neural networks (a generator and a discriminator) that are trained together. The generator tries to produce data that is indistinguishable from real data, and the discriminator tries to distinguish between real and fake data.
   • Boltzmann Machines:
     • A stochastic recurrent neural network that learns to capture complex patterns in the data by minimizing the energy function.

Applications of Unsupervised Learning:

• Market Basket Analysis (finding frequently bought together items)
• Anomaly detection (fraud detection, network security)
• Customer segmentation
• Data compression and feature extraction
• Recommendation systems (based on clustering or associations)

Each algorithm has its strengths and weaknesses depending on the type of data and the problem you’re trying to solve.