Clustering Algorithms Comparison for University of Anbar Researchers’ Google Scholar Profiles

Clustering is an important web mining technique that is extensively used in a variety of fields, including pattern recognition, image processing, and data analysis. K-means and K-medoids are two popular clustering algorithms that attempt to group data points that are similar into clusters. Although both algorithms have the same fundamental goal, they differ in their approaches to cluster formation and the selection of cluster centers. The K-means and K-medoids algorithms are compared in this study in terms of their strengths and weaknesses, complexity, and applicability to real-world issues. The K-Means algorithm appears to be performing better than the K-Mediods algorithm, with a higher silhouette score, Calinski-Harabasz index, and Davies-Bouldin index. The purpose of this paper is to investigate the efficacy of two popular clustering algorithms, k-means and k-medoids. In our scenario, we fetched a dataset from the Google Scholar profiles of researchers associated with the Iraqi University of Anbar. The email "uoanbar.edu.iq" was used to retrieve the dataset used in this research. The study compares the two algorithms’ performance in terms of output and measurement evaluation, to determine which algorithm is better suited to the situation at hand. Our findings show that k-means clustering outperforms k-medoids in terms of output quality as well as computational efficiency, making it the preferred algorithm for this situation. The research adds to the increasing body of knowledge about clustering algorithms and their applicability to real-world datasets.