A Deterministic Seeding Approach for k-means Clustering

Authors

  • Omar Kettani  Scientific Institute, Mohammed V University, Rabat, Morocco

DOI:

https://doi.org/10.32628/CSEIT217246

Keywords:

clustering, k-means, initialization, KKZ, silhouette

Abstract

In this work, a simple and efficient approach is proposed to initialize the k-means clustering algorithm. The complexity of this method is O(nk), where n is the number of data and k the number of clusters. Performance evaluation was done by applying this approach on various benchmark datasets and comparing with the related deterministic KKZ seed algorithm. Experimental results have demonstrated that this approach produces more consistent clustering results in term of average silhouette index.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-04-30

Issue

Volume 7, Issue 2, March-April-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

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

How to Cite

[1]
Omar Kettani, " A Deterministic Seeding Approach for k-means Clustering" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 2, pp.192-195, March-April-2021. Available at doi : https://doi.org/10.32628/CSEIT217246