Unsupervised Learning & Dimensionality Reduction (Weeks 9)
The chapter delves into unsupervised learning techniques, particularly focusing on clustering methods, including K-Means, Hierarchical Clustering, and DBSCAN. It introduces key concepts such as the iterative nature of K-Means, the advantages of not requiring pre-specified clusters in Hierarchical methods, and the distinctive capabilities of DBSCAN in discovering complex shapes and outliers. The chapter emphasizes the importance of proper data preprocessing and evaluation of clustering performance through methods such as the Elbow and Silhouette methods.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Unsupervised learning involves data without predefined labels, allowing machines to discover underlying patterns.
- K-Means clustering requires prior specification of clusters and is sensitive to initial placement.
- DBSCAN effectively identifies clusters of arbitrary shapes and recognizes outliers as noise.
Key Concepts
- -- Unsupervised Learning
- A type of machine learning where the model learns from unlabeled data to identify patterns and structures.
- -- KMeans Clustering
- An iterative algorithm that partitions data into K distinct clusters based on proximity to centroids.
- -- DBSCAN
- A density-based clustering algorithm that identifies clusters based on local density and distinguishes outliers.
- -- Silhouette Analysis
- A method for measuring how similar a data point is to its own cluster compared to other clusters.
- -- Dendrogram
- A tree-like diagram that visually represents the arrangement of clusters formed through hierarchical clustering.
Additional Learning Materials
Supplementary resources to enhance your learning experience.