# Lecture Notes for Cluster Analysis

Lecture 1: Measures of Similarity

Lecture 2.1: Distance Measures Reasoning

Lecture 2.2: Principle Component Analysis Pt. 1

Lecture 3: Discussion of Dataset

Lecture 4: Principal Component Analysis Pt. 2

Lecture 4.2: Revisiting Measures

Lecture 4.3: Cluster Tendency

Lecture 5: Introduction to Connectivity Based Models

Lecture 6: Agglomerative Methods

Lecture 7: Divisive Methods Part 1: Monothetic

Lecture 8: Divisive Methods Part 2: Polythetic

Lecture 9.1: CURE and TSNE

Lecture 9.2: Cluster Validation Part I

Lecture 10.1: Silhouette Coefficient

Lecture 10.2: Centroid-Based Clustering

Lecture 10.3: Voronoi Diagrams

Lecture 11.1: K-means++

Lecture 11.2: K-medoids

Lecture 11.3: K-medians

Lecture 12: Introduction to Density Based Clustering