Clustering

This chapter provides a comprehensive overview of traditional clustering algorithms, which have been fundamental in the field of unsupervised learning. The strengths and limitations of each algorithm are carefully examined, along with guidelines for selecting the most appropriate method based on the dataset characteristics and clustering objectives. Moreover, the main evaluation metrics used in clustering such as cluster validity or popular indexes are presented. By the end of this chapter, readers will gain a deep understanding of traditional clustering algorithms, enabling them to apply these techniques effectively in diverse real-world scenarios and providing a solid foundation for further exploration of more advanced clustering methodologies.