The Ultimate Guide To Partitioning Clustering

September 6, 2017
By

(This article was first published on Easy Guides, and kindly contributed to R-bloggers)

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

Partitioning clustering methods

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:

K means clustering plots

Licence: Licence Creative Commons

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