The article discusses different types of clustering algorithms, focusing on hierarchical clustering techniques. These techniques divide data into nested partitions, either by merging clusters (agglomerative) or splitting them (divisive). Agglomerative clustering starts with individual objects in clusters and merges them based on similarity, while divisive clustering starts with all objects in one cluster and splits them. Both methods have drawbacks, such as incorrect grouping and varying results with different similarity measures. Hierarchical clustering can be visualized using dendrograms, which show the relationships between clusters. Different representations, like pictures or abstract symbols, can be used to interpret hierarchical clusterings.