Clustering Introduction
What Is Clustering?
Clustering is a set of techniques used to partition data into groups, or clusters. Clusters are loosely defined as groups of data objects that are more similar to other objects in their cluster than they are to data objects in other clusters. In practice, clustering helps identify two qualities of data:
- Meaningfulness
- Usefulness
Meaningful clusters expand domain knowledge. For example, in the medical field, researchers applied clustering to gene expression experiments. The clustering results identified groups of patients who respond differently to medical treatments.
Useful clusters, on the other hand, serve as an intermediate step in a data pipeline. For example, businesses use clustering for customer segmentation. The clustering results segment customers into groups with similar purchase histories, which businesses can then use to create targeted advertising campaigns.
Application of Clustering: There are many other applications of clustering, such as document clustering and social network analysis. These applications are relevant in nearly every industry, making clustering a valuable skill for professionals working with data in any field.
Overview of Clustering Techniques
You can perform clustering using many different approaches—so many, in fact, that there are entire categories of clustering algorithms. Each of these categories has its own unique strengths and weaknesses. This means that certain clustering algorithms will result in more natural cluster assignments depending on the input data.
Selecting an appropriate clustering algorithm for your dataset is often difficult due to the number of choices available. Some important factors that affect this decision include the characteristics of the clusters, the features of the dataset, the number of outliers, and the number of data objects.
You’ll explore how these factors help determine which approach is most appropriate by looking at three popular categories of clustering algorithms:
- Partitional clustering (Eg. kmeans)
- Hierarchical clustering (Eg. AGNES, DIANA)
- Density-based clustering (Eg. DBSCAN)
Partitional Clustering
Partitional clustering divides data objects into nonoverlapping groups. In other words, no object can be a member of more than one cluster, and every cluster must have at least one object.
These techniques require the user to specify the number of clusters, indicated by the variable k. Many partitional clustering algorithms work through an iterative process to assign subsets of data points into k clusters. Two examples of partitional clustering algorithms are k-means and k-medoids.
These algorithms are both nondeterministic, meaning they could produce different results from two separate runs even if the runs were based on the same input.
Partitional clustering methods have several strengths:
- They work well when clusters have a spherical shape.
- They’re scalable with respect to algorithm complexity.
They also have several weaknesses:
- They’re not well suited for clusters with complex shapes and different sizes.
- They break down when used with clusters of different densities.