In Machine Learning, clustering involves identifying similar instances and then assigning them to similar clusters or groups of instances. It is an unsupervised machine learning technique. In this article, I’ll give you an introduction to the most popular clustering algorithms in machine learning and their implementation using Python.
Clustering means assigning each instance to a group or a cluster of similar instances. It’s like classification, but it’s the job of unsupervised machine learning. It is used in a wide variety of machine learning applications such as:
- Customer Segmentation: When you need to group your customers based on their purchases and other buying habits.
- Data Analysis: When you want to analyze a new dataset, it is very useful to identify patterns when you analyze each cluster in the dataset.
- Anomaly Detection: When you analyze clusters and find that some data points have a very low affinity with all the clusters, those data points are anomalies.
There are more uses of clustering algorithms in machine learning. Whenever you are dealing with a dataset that has characteristics related to segments or groups like income groups, age groups, etc., you can use clustering algorithms.
Most Popular Clustering Algorithms in Machine Learning
Like classification and regression, Clustering also has a bunch of algorithms that we can use, but the most popular ones are K-means and DBSCAN. Let’s have a look at both these algorithms:
- K-Means: The K-Means Clustering is a clustering algorithm capable of clustering an unlabeled dataset quickly and efficiently in just a very few iterations. It works by labelling all instances on the cluster with the closest centroid. When the instances are centred around a particular point, that point is called a centroid. You can learn the implementation of this algorithm using Python from here.
- DBSCAN: DBSCAN stands for Density-Based Spatial Clustering for Applications with Noise. This is an unsupervised clustering algorithm that is used to find high-density base samples to extend the clusters. The DBSCAN clustering algorithm works well if all the clusters are dense enough and are well represented by the low-density regions. You can learn the implementation of this algorithm using Python from here.
So, in machine learning, clustering is the task of unsupervised machine learning, which involves grouping similar instances together. There are many clustering algorithms, but the most popular clustering algorithms are K-Means and DBSCAN clustering. Hope you liked this article on the most popular clustering algorithms in machine learning and their implementation using Python. Please feel free to ask your valuable questions in the comments section below.