Articles - Cluster Validation Essentials

The cluster validation consists of measuring the goodness of clustering results. Before applying any clustering algorithm to a data set, the first thing to do is to assess the clustering tendency. That is, whether applying clustering is suitable for the data. If yes, then how many clusters are there. Next, you can perform hierarchical clustering or partitioning clustering (with a pre-specified number of clusters). Finally, you can use a number of measures, described in this part, to evaluate the goodness of the clustering results.

cluster validation essentials

Contents

Assessing Clustering Tendency

  • Required R packages
  • Data preparation
  • Visual inspection of the data
  • Why assessing clustering tendency?
  • Methods for assessing clustering tendency
    • Statistical methods
    • Visual methods

Determining The Optimal Number Of Clusters

  • Elbow method
  • Average silhouette method
  • Gap statistic method
  • Computing the number of clusters using R
    • Required R packages
    • Data preparation
    • fviz_nbclust() function: Elbow, Silhouhette and Gap statistic methods
    • NbClust() function: 30 indices for choosing the best number of clusters

Cluster Validation Statistics

  • Internal measures for cluster validation
    • Silhouette coefficient
    • Dunn index
  • External measures for clustering validation
  • Computing cluster validation statistics in R
    • Required R packages
    • Data preparation
    • Clustering analysis
    • Cluster validation
    • External clustering validation

Choosing the Best Clustering Algorithms

  • Measures for comparing clustering algorithms
  • Compare clustering algorithms in R

Computing P-value for Hierarchical Clustering

  • Description of pvclust() function
  • Usage of pvclust() function

Related Book:


Practical Guide to Cluster Analysis in R
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