Euclidean, Manhattan and Minkowski Distance Methods For Clustering Algorithms

Authors

  • Aye Aye Thant  Information Technology Supporting and Maintenance Department, Computer University (Mandalay), Mandalay, Myanmar
  • Soe Moe Aye  Information Technology Supporting and Maintenance Department, Computer University (Mandalay), Mandalay, Myanmar

DOI:

https://doi.org/10.32628/IJSRSET2073118

Keywords:

Clustering, dissimilarities, interval-scaled variables, Euclidean, Manhattan, Minkowski

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.

References

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2020-06-30

Issue

Volume 7, Issue 3, May-June-2020

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Aye Aye Thant, Soe Moe Aye "Euclidean, Manhattan and Minkowski Distance Methods For Clustering Algorithms" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 3, pp.553-559, May-June-2020. Available at doi : https://doi.org/10.32628/IJSRSET2073118