Statistical Analysis and Modeling in R: Performing Clustering

R Programming 4.0+
  • 7 Videos | 52m 14s
  • Includes Assessment
  • Earns a Badge
Clustering is an unsupervised learning algorithm that self-discovers patterns in data and helps identify logical groupings. Use this course to distinguish between supervised and unsupervised learning and recognize how regression and classification algorithms differ from clustering. Examine the basic principles of clustering models and how k-means clustering finds logical groupings in your data. Learn the evaluation techniques used in clustering and find the optimal number of clusters in your data using both the elbow method and the Silhouette score. Perform clustering on a dataset with multiple attributes and visualize clusters in your data using principal components. When you've completed this course, you'll be able to find groupings in your data using k-means clustering and compute the optimal number of clusters for your data.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    recall the techniques used to evaluate clustering models
    investigate and visualize data before fitting a model
    perform k-means clustering and interpret clustering results
  • find the optimal number of clusters using the elbow method and Silhouette score
    perform k-means clustering on multi-attribute data
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 10s
    UP NEXT
  • Playable
    2. 
    Recognizing and Evaluating Clustering Models
    11m 55s
  • Locked
    3. 
    Investigating and Visualizing Clustering Data in R
    3m 46s
  • Locked
    4. 
    Performing K-means Clustering, Interpreting Results
    10m 25s
  • Locked
    5. 
    Using R to Find the Optimal Number of Clusters
    10m 56s
  • Locked
    6. 
    Using K-means Clustering on Multi-attribute Data
    8m 23s
  • Locked
    7. 
    Course Summary
    2m 9s

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