Unsupervised Machine Learning and Beyond Machine Learning

Unsupervised machine learning endeavors to unveil latent patterns and groupings within datasets. It remains an actively evolving field, teeming with numerous experimental methods that continue to emerge. This chapter serves to introduce the widely employed techniques of unsupervised machine learning. Moreover, the significance of graph data is elucidated, as it holds the potential to serve as a fundamental element in the realms of materials and catalysts informatics. By harnessing the power of graph-based representations, researchers can gain deeper insights into the underlying structures and relationships within these domains. Consequently, the exploration of graph data assumes a pivotal role in driving advancements and facilitating breakthroughs in materials and catalysts informatics.