Organizing clusters as a hierarchical tree

Introduction

In this article, we will take a look at an alternative approach to K Means clustering, popularly known as the Hierarchical Clustering. The hierarchical Clustering technique differs from K Means or K Mode, where the underlying algorithm of how the clustering mechanism works is different. K Means relies on a combination of centroid and euclidean distance to form clusters, hierarchical clustering on the other hand uses agglomerative or divisive techniques to perform clustering. Hierarchical clustering allows visualization of clusters using dendrograms that can help in better interpretation of results through meaningful taxonomies. Creating a dendrogram doesn’t require us to specify the number of clusters upfront.

Programming languages like R, Python, and SAS allow hierarchical clustering to work with categorical data making it easier for problem statements with categorical variables to deal with.

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