This chapter introduces clustering, an unsupervised machine learning method used to identify hidden structures in data without relying on predefined labels. By grouping similar observations into clusters, clustering can reveal patterns and groupings within a dataset. In this chapter, we will explore two widely used clustering methods: hierarchical clustering and K-means clustering. We will also discuss heatmaps, a graphical tool that employs hierarchical clustering to visually represent patterns in the data. Heatmaps use color gradients to highlight relationships within the dataset, making it easier to detect patterns and understand why objects cluster.