Several types of clustering algorithms can be used depending on the nature of the data and the desired clustering outcome. Here are some common types of clustering:

1. Partitional clustering: This clustering method splits the information into multiple groups based on the characteristics and similarities of the data. Given a data set of N points, a partitioning method constructs K (N ≥ K) partitions of the data, with each partition representing a cluster. K must be chosen manually according to the specialty of data and domain area. The most popular partitional clustering algorithm is K-means;

2. Hierarchical Clustering: In this type of clustering, the goal is to create a tree-like structure of nested clusters, where each cluster can contain individual data points or other clusters. Using this tree-like structure, we can understand in what sequence exactly which points are merged. Hierarchical clustering can be further divided into two subtypes: agglomerative clustering and divisive clustering;

3. Density-based clustering: this type of clustering identifies clusters based on the density of data points in the feature space. The goal of density-based clustering is to find areas of high density separated by areas of low density. The most popular density-based clustering algorithms are DBSCAN and Mean-shift.

There are also other types of clustering but they will not be covered in this course