Euclidean, Manhattan and Minkowski Distance Methods For Clustering Algorithms

Abstract

The process of grouping a set of physical objects into classes of similar objects is called clustering. Clustering is a process of grouping the data into classes or cluster so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. Dissimilarities are assessed based on the attribute values describing the objects. This system studies how to compute dissimilarities between objects represented by interval scaled variables. This system is intended to implement the dissimilarity matrix for interval-scaled variables using Euclidean, Manhattan, and Minkowski distance methods. This stores a collection of proximities that are available for all pairs of n objects.