Unsupervised Learning

Unsupervised Learning involves training models on unlabeled data, aiming to discover patterns, relationships, or structures within the dataset.

Clustering

Clustering is an unsupervised learning technique where the algorithm groups similar data points together based on inherent patterns or similarities, aiming to discover the underlying structure of the data.

Some clustering techniques include:

• K-Means Clustering

• Hierarchical Clustering

• DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

• Agglomerative Clustering

• Gaussian Mixture Model (GMM)

• Affinity Propagation

• Mean Shift Clustering

• Spectral Clustering

• Fuzzy C-Means (FCM) Clustering

• Birch (Balanced Iterative Reducing and Clustering using Hierarchies)

Examples of applications include:

• Customer Segmentation

• Anomaly Detection

• Social Network Analysis

• Document Clustering

• Recommendation Systems

Dimensionality Reduction

Dimensionality Reduction is a technique used to reduce the number of input features in a dataset while retaining essential information. It helps in simplifying the model and improving computational efficiency.

Some techniques of dimensionality reduction include:

• Principal Component Analysis (PCA)

• t-Distributed Stochastic Neighbor Embedding (t-SNE)

• Uniform Manifold Approximation and Projection (UMAP)

• Singular Value Decomposition (SVD)

• Independent Component Analysis (ICA)

• Linear Discriminant Analysis (LDA)

• Autoencoders (Neural Network-based)

• Isomap (Isometric Mapping)

• Locally Linear Embedding (LLE)

• Multi-Dimensional Scaling (MDS)

Examples of applications include:

• Image Compression

• Feature Engineering

• Document Retrieval

• Genomic Data Analysis

• Visualization in High-Dimensional Data