Therefore, the number of clusters at the start will be K, while K is an integer representing the number of data points. At the start, treat each data point as one cluster.Steps to Perform Hierarchical Clusteringįollowing are the steps involved in agglomerative clustering: In this article we will focus on agglomerative clustering that involves the bottom-up approach. In the former, data points are clustered using a bottom-up approach starting with individual data points, while in the latter top-down approach is followed where all the data points are treated as one big cluster and the clustering process involves dividing the one big cluster into several small clusters. There are two types of hierarchical clustering: Agglomerative and Divisive. Before implementing hierarchical clustering using Scikit-Learn, let's first understand the theory behind hierarchical clustering. In some cases the result of hierarchical and K-Means clustering can be similar. Like K-means clustering, hierarchical clustering also groups together the data points with similar characteristics. Hierarchical clustering is a type of unsupervised machine learning algorithm used to cluster unlabeled data points.
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