Hierarchical Clustering - An Unsupervised Learning Algorithm
Introduction Unsupervised learning is a type of Machine learning in which we use unlabeled data and we try to find a pattern among the data. Clustering algorithms falls under the category of unsupervised learning. In these algorithms, we try to make different clusters among the data. Hierarchical Clustering algorithms build a hierarchy of clusters where each node is a cluster consisting of the clusters of its children node. To check it's implementation in Python CLICK HERE There are various strategies in Hierarchical Clustering such as : Divisive Agglomerative This type of diagram is called Dendrogram. Divisive - It is a Top-down approach. So we start with all observations in a large cluster and break it down into smaller ones. Agglomerative - It is the opposite of Divisive as it is a Bottom-Up approach. Here, each observation starts in its cluster and pairs of the cluster are merged as they move up the hierarchy. (Generally, Agglomerative is used more as compared to Divisive