Understanding Clustering in Machine Learning: Algorithms and Use Cases

Clustering is an unsupervised machine learning method, where datapoints are organized into groups, or clusters, consisting of similar datapoints. This divides them such that each datapoint has less or no similarities with another cluster.

What is clustering in machine learning?

Clustering is performed by specific machine learning algorithms, which scan a dataset, and places each datapoint it in a cluster of other datapoints with similar features.

Clustering vs classification

Clustering is similar to classification in that it identifies patterns within data. However, classification is a supervised learning method, where training data is required to be labeled beforehand, and clustering is unsupervised and does not require labeled data.

Unsupervised learning

Unsupervised machine learning algorithms are self-learning and learn from data without human supervision. It uses an unlabeled dataset and infers the structure based on similarities, differences, and patterns within the data.

Because clustering is unsupervised, it is often used in exploratory data analysis to break down complex data and discover new patterns.

Types of clustering

Clustering can be categorized into two main types; Hard clustering results in datapoints belonging entirely to one cluster, while soft clustering gives a probability that a datapoint belongs in each predefined cluster.

Clustering algorithms

As with most machine learning methods, there are various clustering algorithms that go about the task in different ways. Different algorithms will often yield significantly different results, even for the same input data.

K-means clustering

K-means clustering is one of the most widely used algorithms due to its applicability to a wide range of uses, ease of implementation, and speed. It divides the data into k clusters, with each datapoint assigned to the cluster based on the closest centroid.

Hierarchical Clustering

Hierarchical clustering is a connectivity-based algorithm, where a hierarchy, or tree, of clusters is created. Each datapoint starts off as its own cluster at the bottom of the tree, and the two closest datapoints form a cluster according to distance.

Mean-shift clustering

Mean-shift clustering is a density-based algorithm that iteratively assigns datapoints to clusters and automatically determines the number of clusters based on proximity to a centroid.

DBSCAN Clustering

DBSCAN stands for Density-Based Spatial Clustering Applications with Noise and works on the premise that high-density spaces (clusters) are separated by low-density spaces (noise). It requires two inputs: Epsilon (eps) and MinPts, which specify minimum neighbors for categorization into core, border, or noise points.

Clustering use cases

Segmenting customers

Clustering can be used to segment customers into groups that share similar traits or behaviors, allowing for more personalized marketing and communication.

Fraud detection

Fraud detection is employed in financial services to detect and flag suspicious transactions. Clustering can identify outlier transactions that deviate from expected patterns.

Recommendation engines

Recommendation engines utilize clustering for product recommendations on e-commerce sites, suggesting movies on streaming services, or recommending articles from news sites. K-means clustering is commonly used in these systems.

Wrapping up

This blog has provided insight into clustering in machine learning. If you’re looking to start building clustering models, consider using the FlexiBuild Studio. If you need assistance with machine learning, reach out with your business problem.