Silhouette Analysis & Hierarchical Clustering in Machine Learning
Explore the concept of Silhouette Analysis for evaluating clustering quality and the methodology of Hierarchical Clustering in machine learning. Understand types of Hierarchical Clustering such as Agglomerative approach, linkage criteria, Ward's Method, and Dendrogram visualization. Discover real-world applications in genomics, social network analysis, market segmentation, and image segmentation.
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Dr. SNS RAJALAKSHMI COLLEGE OF ARTS & SCIENCE (Autonomous) Coimbatore -641049 DEPARTMENT OF COMPUTER APPLICATIONS(PG) COURSE NAME : 22UDA804 - Basics of Machine Learning II CS DA /II SEMESTER Unit III Topic 4.silhouttes hierarchical clustering 3/15/2024 Software Process Improvement
4.silhouttes hierarchical clustering 1. Silhouette Analysis A technique to evaluate clustering quality by measuring how similar each point is to its own cluster vs. other clusters. Silhouette Score (ranges from -1 to 1): 1 Well-clustered, clear separation. 0 Overlapping clusters. -1 Misclassified points (wrong cluster). AWS FUNDAMENTALS
4.silhouttes hierarchical clustering 2. Hierarchical Clustering A tree-based clustering method that builds a hierarchy of clusters. No need to predefine the number of clusters (unlike K-Means). AWS FUNDAMENTALS
4.silhouttes hierarchical clustering Types of Hierarchical Clustering: a. Agglomerative (Bottom-Up Approach) Starts with each data point as a separate cluster. Iteratively merges the closest clusters until one cluster remains. Uses linkage criteria to measure distance between clusters: Single Linkage: Minimum distance between clusters. Complete Linkage: Maximum distance between clusters. Average Linkage: Average distance between all points in clusters. Ward s Method: Minimizes variance within clusters. AWS FUNDAMENTALS
4.silhouttes hierarchical clustering 3. Dendrogram (Cluster Visualization) A tree-like diagram that shows the merging of clusters at each step. Cutting the dendrogram at an optimal height determines the final number of clusters. AWS FUNDAMENTALS
4.silhouttes hierarchical clustering AWS FUNDAMENTALS
4.silhouttes hierarchical clustering AWS FUNDAMENTALS
4.silhouttes hierarchical clustering 6. Real-World Applications Genomics & Biology (DNA sequence clustering). Social Network Analysis (Community detection). Market Segmentation (Customer behavior grouping). Image Segmentation (Grouping pixels based on similarity). AWS FUNDAMENTALS
