Hierarchical Clustering and Dendrograms in R for Data Science

 By Medium - 2021-03-18

Description

In the early stages of performing data analysis, an important aspect is to get a high level understanding of the multi-dimensional data and find some sort of pattern between the different variables…

Summary

  • A guide to understanding clustering techniques, its applications, pros & cons and creating Dendrograms in R. In the early stages of performing data analysis, an important aspect is to get a high level understanding of the multi-dimensional data and find some sort of pattern between the different variables- this is where clustering comes in.
  • ` · Divisive (Top-down) : Call hclust() This forms a hierarchical cluster of the data points based on a distance metric (in this case ‘Euclidean’) on the set of objects in the dataset (in this case 10) cluster <- hclust(distxy) Image by author Step 4: From the classification of animal/plant species to determining the similarities in the variants of a virus to categorizing customer segmentation for marketing campaigns, Dendrograms has many uses.

 

Topics

  1. Backend (0.29)
  2. Database (0.16)
  3. Machine_Learning (0.13)

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