Clustering with Euclidean Distance, Manhattan - Distance, Mahalanobis - Euclidean Distance, and Chebyshev Distance with Their Accuracy

Authors

  • Said Al Afghani PT. Pegadaian (Persero), Jakarta Selatan, 12910, Indonesia
  • Widhera Yoza Mahana Putra PT. Pegadaian (Persero), Jakarta Selatan, 12910, Indonesia

DOI:

https://doi.org/10.29244/ijsa.v5i2p369-376

Keywords:

accuracy, algorithm, clustering, k-means

Abstract

There are several algorithms to solve many problems in grouping data. Grouping data is also known as clusterization, clustering takes advantage to solve some problems especially in business. In this note, we will modify the clustering algorithm based on distance principle which background of K-means algorithm (Euclidean distance). Manhattan, Mahalanobis-Euclidean, and Chebyshev distance will be used to modify the K-means algorithm. We compare the clustered  result related to their accuracy, we got Mahalanobis - Euclidean distance gives the best accuracy on our experiment data, and some results are also given in this note.

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References

Bindra, K., & Mishra, A. (2017). A Detailed Study of Clustering Algorithms. 2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), 371–376. IEEE.

Roshan Sharma. (2020). Mall Customers Clustering Analysis.

scikit-learn developers. (2017). calinski_harabaz_score.

Singh, A., Yadav, A., & Rana, A. (2013). K-means with Three Different Distance Metrics. International Journal of Computer Applications, 67(10).

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Published

2021-06-30

How to Cite

Afghani, S. A. ., & Putra, W. Y. M. . (2021). Clustering with Euclidean Distance, Manhattan - Distance, Mahalanobis - Euclidean Distance, and Chebyshev Distance with Their Accuracy. Indonesian Journal of Statistics and Its Applications, 5(2), 369–376. https://doi.org/10.29244/ijsa.v5i2p369-376

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Articles