Clustering Techniques

  • 10 Videos | 40m 23s
  • Includes Assessment
  • Earns a Badge
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The key to meaningful analysis is the ability to choose the right methods that provide the greatest predictive power. Discover how data clustering, such as K-Means, hierarchical, and DBSCAN, is used to combine similar subsets of data.

WHAT YOU WILL LEARN

  • recognize characteristics of clustering
    identify the different types of clustering
    calculate proximity
    list key features of K-Means Clustering
    recognize key steps for reducing the sum of squared errors in K-Means Clustering
  • recognize key steps for the termination of K-Means Clustering iterations
    evaluate K-Means Clustering
    list key features of Hierarchical Clustering and DBSCAN
    recognize key steps in DBSCAN
    perform DBSCAN

IN THIS COURSE

  • Playable
    1. 
    Introduction to Clustering
    4m
    UP NEXT
  • Playable
    2. 
    Types of Clustering Techniques
    4m 18s
  • Locked
    3. 
    Proximity Measures for Clustering
    4m
  • Locked
    4. 
    Overview of K-Means Clustering
    2m 52s
  • Locked
    5. 
    Minimizing SSE of Data Points
    4m 42s
  • Locked
    6. 
    K-Means Clustering Termination Procedures
    1m 59s
  • Locked
    7. 
    Evaluation and Considerations for K-Means Clustering
    3m 55s
  • Locked
    8. 
    Hierarchical Clustering and DBSCAN Overview
    4m 18s
  • Locked
    9. 
    DBSCAN Operation
    2m 16s
  • Locked
    10. 
    DBSCAN Attributes
    3m 4s

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