Hierarchical Clustering: A Survey

Abstract

There is a need to scrutinise and retrieve information from data in today's world. Clustering is an analytical technique which involves dividing data into groups of similar objects. Every group is called a cluster, and it is formed from objects that have affinities within the cluster but are significantly different to objects in other groups. The aim of this paper is to look at and compare two different types of hierarchical clustering algorithms. Partition and hierarchical clustering are the two main types of clustering techniques. Hierarchical clustering algorithm is one of the algorithms discussed here. The aforementioned algorithms are described and analysed in terms of factors such as dataset size, data set type, number of clusters formed, consistency, accuracy, and efficiency. Hierarchical clustering is a cluster analysis technique that aims to create a hierarchy of clusters. A hierarchical clustering method is a set of simple (flat) clustering methods arranged in a tree structure. These methods create clusters by recursively partitioning the entities in a top-down or bottom-up manner. We examine and compare hierarchical clustering algorithms in this paper. The intent of discussing the various implementations of hierarchical clustering algorithms is to assist new researchers and beginners to understand how they function, so they can come up with new approaches and innovations for improvement.