Lesson Archives

  1. Fuzzy clustering is also known as soft method. Standard clustering (K-means, PAM) approaches produce partitions, in which each observation belongs to only one cluster. This is known as hard clustering. In Fuzzy clustering, items can be a member of more than one cluster. Each item has a set of membership coefficients corresponding to the degree of being in a given cluster. In this article, we’ll describe how to compute fuzzy clustering using the R software.
  2. In this article, we’ll start by describing the different measures in the clValid R package for comparing clustering algorithms. Next, we’ll present the function clValid(). Finally, we’ll provide R scripts for validating clustering results and comparing clustering algorithms.
  3. In this article, we start by describing the different methods for clustering validation. Next, we'll demonstrate how to compare the quality of clustering results obtained with different clustering algorithms. Finally, we'll provide R scripts for validating clustering results.
  4. In this chapter, we start by describing why we should evaluate the clustering tendency before applying any clustering method on a data. Next, we provide statistical and visual methods for assessing the clustering tendency in R software.