Comparative Performance Analysis of K-Means and DBSCAN Clustering algorithms on various platforms
Nafi Shahriar, Shafait Faisal, Md. Masfakuzzaman Pinjor, Md Al Rafi, Atiquer Rahman Sarkar · 2019
While many data scientists are working hard just to improve a very fractional amount of performance, we wonder if there are any difference in performance of clustering among the platform we normally use. So we selected three datasets and perform K-means and DBSCAN clustering algorithm on the selected datasets in the four most popular platforms- Python, Matlab, R and Wolfram Mathematica. Then we summarized the results and compare the performances of different platforms. For performance metrics we used 2 criteria, first one is how much time it takes to execute clustering function and second one is the overall accuracy of clustering result. In our study we found that algorithm takes different execution time in different platform. Also variation was observed in terms of accuracy on various platform. Sometimes execution time was correlated with dataset size. Finally we suggest the platforms that are to be used for speed and for accuracy.