A Comparative Study of K-means Clustering Algorithms Using Euclidean and Manhattan Distance for Climate Data.

The K-means clustering algorithms (Random, K-means++, Canopy, and Farthest First) are unsupervised machine learning techniques designed to group data points based on their similarities. The study examined the effects of clustering algorithms and distance metrics on climate data analysis from meteorological stations in the Kurdistan Region of Iraq (2020–2022). 8-attribute dataset with 1,095 cases was clustered using Random, K-means++, Canopy, and Farthest First methods, evaluated with Euclidean and Manhattan distance metrics via the WEKA tools, which is a versatile and accessible open-source tool for machine learning and data mining. It features a user-friendly interface, a wide range of algorithms, robust pre-processing and visualization tools, and cross-platform compatibility.Focusing on efficiency and reducing variation within clusters, the results revealed that within Euclidean distance, all algorithms formed two clusters. Canopy required the most iterations, Farthest First the fewest. K-means++ was the fastest, Canopy the slowest. WCSS values were similar, with Random and Canopy scoring lowest, but within Manhattan Distance, all algorithms again formed two clusters. Canopy had the highest iterations, Farthest First the fewest and fastest, while Random was slowest. WCSS differences were negligible, with Random, Canopy, and Farthest First performing best.Graphs illustrate the highlighted differences in cluster distribution, iterations, execution time, and WCSS. Euclidean distance yielded lower WCSS, while interactive maps revealed clearer cluster distributions for most attributes compared to Manhattan distance. produced the lowest within-cluster sum of squared errors compared to the Manhattan distance.