Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)

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• AI Solver
  • ...find outliers
  • ...cluster
• Capabilities
• Hierarchical Cluster Analysis (HCA)
• Hierarchical Temporal Memory (HTM)
• K-Means
• How to Perform Hierarchical Clustering using R | Perceptive Analytics
• Exploreing K-Means with Internal Validity Indexes for Data Clustering in Traffic Management System | S. Nawrin, S. Akhter and M. Rahatur

Hierarchical clustering algorithms actually fall into 2 categories:

1. Agglomerative (HAC - AGNES); bottom-up
2. Divisive (HDC - DIANA); top-down

Agglomerative Clustering - Bottom Up

Bottom-up algorithms treat each data point as a single cluster at the outset and then successively merge (or agglomerate) pairs of clusters until all clusters have been merged into a single cluster that contains all data points. Bottom-up hierarchical clustering is therefore called hierarchical agglomerative clustering or HAC. This hierarchy of clusters is represented as a tree (or dendrogram). The root of the tree is the unique cluster that gathers all the samples, the leaves being the clusters with only one sample. The 5 Clustering Algorithms Data Scientists Need to Know | Towards Data Science

Hierarchical clustering does not require us to specify the number of clusters and we can even select which number of clusters looks best since we are building a tree. Additionally, the algorithm is not sensitive to the choice of distance metric; all of them tend to work equally well whereas with other clustering algorithms, the choice of distance metric is critical. A particularly good use case of hierarchical clustering methods is when the underlying data has a hierarchical structure and you want to recover the hierarchy; other clustering algorithms can’t do this. These advantages of hierarchical clustering come at the cost of lower efficiency, as it has a time complexity of O(n³), unlike the linear complexity of K-Means and GMM.