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