Data Clustering Lecture

Abstract

This lecture overviews Data Clustering  that has many applications in e.g., facial image clustering, signal/image clustering, concept creation.  It covers the following topics in detail: Clustering Definitions. Distance measures, Mahalanobis distance, Euclidean distance, Lp norm, L1 Norm  Similarity measures, Cosine similarity, Correlation coefficient. Distance Functions between a Point and a Set. Distance Functions between two Sets. Clustering algorithm categories: Exhaustive Clustering. Sequential Clustering, Maximin algorithm. Clustering by optimization, K-means algorithm, ISODATA algorithm. Fuzzy clustering. Vector Quantization, Voronoi regions, LVQs. Graph-based clustering, N-Cut Graph Clustering, Spectral graph clustering.

Facial image clustering.

Data-Clustering-v3.5.3

Understanding Questionnaire

https://docs.google.com/forms/data-clustering

Tutorial Exercises

1. Data Clustering Tutorial Exercises
2. 1. Data Clustering with k-means Algorithm Tutorial Exercise
   2. Data Clustering with Maximin Algorithm Tutorial Exercise

Programming Exercises

1. Data Clustering
   1. Data Clustering with k-Means algorithm
   2. Data Clustering with Mean Shift algorithm