Types Of ClusteringTypes Of Clustering

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


Imagine that you have to cluster people based on their family ties and create a family tree. Which type of clustering should you use to deal with this task?

Select the correct answer

Everything was clear?

Section 1. Chapter 3