Clustering Analysis

University of Colorado Boulder via Coursera

Go to Course: https://www.coursera.org/learn/clustering-analysis

Introduction

Understand the principles and significance of unsupervised learning, particularly clustering and dimension reduction.

Apply clustering techniques to diverse datasets for pattern discovery and data exploration.

Implement Principal Component Analysis (PCA) for dimension reduction and interpret the reduced feature space.

Syllabus

Introduction and Partitioning Clustering

This week provides an introduction to unsupervised learning and clustering analysis. You will delve into partitioning clustering methods, such as K-Means and K-Medoids, understanding their principles and applications.

Hierarchical Clustering

This week you will explore hierarchical clustering, a method that creates a tree-like structure to represent data similarities.

Density-based Clustering

This week focuses on density-based clustering, which groups data points based on their density within the dataset.

Grid-based Clustering

Throughout this week, you will explore grid-based clustering, an approach that partitions the data space into grids for efficient clustering.

Dimension Reduction Methods

This week introduces dimension reduction techniques as a critical preprocessing step for handling high-dimensional data.

Case Study

The final week focuses on a comprehensive case study where you will apply clustering and dimension reduction techniques to solve a real-world problem.