A Clustering Algorithm Employing Salp Swarm Algorithm and K-Means
Bibi Aamirah Shafaa Emambocus, Muhammed Basheer Jasser, Lim Cher Zet, Samuel-Soma M. Ajibade, Richard T.K. Wong, Hui Na Chua, Ahmad Sahban Rafsanjani · 2024
Clustering, one of the main types of unsupervised machine learning, consists of grouping data into clusters to discover hidden patterns. Hence it is a crucial machine learning task. The predominant algorithm employed for clustering tasks is the k-means algorithm. However, it has some limitations including being sensitive to the initial centroids. Recently some swarm intelligence algorithms have been noticed to be able to effectively optimize k-means. Hence, in this paper, the Salp Swarm Algorithm (SSA), a recent swarm intelligence with favorable exploration and exploitation capabilities, is employed for optimizing k-means. Specifically, SSA is employed to optimize the initial centroids of k-means to overcome its limitation. The proposed clustering algorithm is applied as part of a movie recommendation system to cluster the users based on their movie preferences. The experimental findings demonstrate that in comparison to the original k-means technique, the proposed clustering algorithm yields superior outcomes as the clustered data by the proposed algorithm has a lower within cluster sum of squares and a higher silhouette score.