부산대학교 도서관

Data clustering has many applications in machine learning, data mining and image processing. K-means is the most popular clustering algorithm due to its efficiency and simplicity of implementation. However, K-means has limitations, such as large feature spaces, which may affect its effectiveness. To improve K-means accuracy, we adopt the Biogeography-Based Optimization (BBO) evolutionary technique to select the most relevant features of datasets. We conducted several experiments to compare our approach with other methods, such as PCA and Particle Swarm Optimization (PSO). The results demonstrate the effectiveness of BBO for feature selection.