Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)
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 Hierarchical Cluster Analysis (HCA)
 Hierarchical Temporal Memory (HTM)
 KMeans
 How to Perform Hierarchical Clustering using R  Perceptive Analytics
 Exploreing KMeans 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:
 Agglomerative (HAC  AGNES); bottomup, first assigns every example to its own cluster, and iteratively merges the closest clusters to create a hierarchical tree.
 Divisive (HDC  DIANA); topdown, first groups all examples into one cluster and then iteratively divides the cluster into a hierarchical tree.
Agglomerative Clustering  Bottom UpBottomup 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. Bottomup 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 KMeans and GMM.
Divisive Clustering = Top Down
