Intermediate Clustering in R

4 hours of instruction

In this course, learners will encounter more sophisticated methods for generating clusters within unlabeled data using R. The first method, hierarchical clustering, creates tree branch-based clusters in order of increasing specificity. The second, density-based clustering, creates groups based on the concentration of data points within a region. By the end of this course, learners will prepare data for, implement, and optimize these models, and compare their relative advantages.

OBJECTIVES

1. Mine data to find latent patterns and groups in different types of data
2. Summarize the process behind agglomerative clustering and discuss the types of linkage methods
3. Summarize the process and implement density-based clustering (DBSCAN)
4. Identify use cases where clustering analyses are relevant and where they are not applicable

PREREQUISITES

Moderate familiarity wrangling and visualizing data based on unsupervised learning methods in R is required.

SYLLABUS & TOPICS COVERED

1. Hierarchical
   • Hierarchical clustering algorithm in a nutshell
   • Implement Hierarchical clustering on a dataset
2. DBSCAN
   • DBSCAN algorithm use case and logic
   • Implement DBSCAN on a dataset

SOFTWARE REQUIREMENTS

You will have access to an R-based Posit Cloud environment for this course. No additional download or installation is required.