Clustering with Euclidean Distance, Manhattan - Distance, Mahalanobis - Euclidean Distance, and Chebyshev Distance with Their Accuracy
Keywords:accuracy, algorithm, clustering, k-means
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.
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).