The Ultimate Guide To Partitioning Clustering

In this first volume of symplyR, we are excited to share our Practical Guides to Partioning Clustering.

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The course materials contain 3 chapters organized as follow:

K-Means Clustering Essentials

Contents:

• K-means basic ideas
• K-means algorithm
• Computing k-means clustering in R
  • Data
  • Required R packages and functions: stats::kmeans()
  • Estimating the optimal number of clusters: factoextra::fviz_nbclust()
  • Computing k-means clustering
  • Accessing to the results of kmeans() function
  • Visualizing k-means clusters: factoextra::fviz_cluster()
• K-means clustering advantages and disadvantages
• Alternative to k-means clustering

K-Medoids Essentials: PAM clustering

Contents:

• PAM concept
• PAM algorithm
• Computing PAM in R
  • Data
  • Required R packages and functions: cluster::pam() or fpc::pamk()
  • Estimating the optimal number of clusters: factoextra::fviz_nbclust()
  • Computing PAM clustering
  • Accessing to the results of the pam() function
  • Visualizing PAM clusters: factoextra::fviz_cluster()

CLARA - Clustering Large Applications

Contents:

• CLARA concept
• CLARA Algorithm
• Computing CLARA in R
  • Data format and preparation
  • Required R packages and functions: cluster::clara()
  • Estimating the optimal number of clusters: factoextra::fviz_nbclust()
  • Computing CLARA
  • Visualizing CLARA clusters: factoextra::fviz_cluster()

Example of plots:

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