Open AccessEuclidean, Manhattan and Minkowski Distance Methods For Clustering Algorithms
Author(s) -
Aye Aye Thant,
Soe Moe Aye
Publication year - 2020
Publication title -
international journal of scientific research in science, engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-1990
pISSN - 2394-4099
DOI - 10.32628/ijsrset2073118
Subject(s) - minkowski distance , cluster analysis , minkowski space , euclidean distance , similarity (geometry) , euclidean geometry , mathematics , distance matrix , euclidean distance matrix , set (abstract data type) , cluster (spacecraft) , interval (graph theory) , algorithm , computer science , pattern recognition (psychology) , artificial intelligence , combinatorics , geometry , image (mathematics) , programming language
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.