Clustering is the task of identifying similar instances based on similar features and assigning them to clusters based on similar instances. It sounds like classification where each instance is also assigned to a group, but unlike classification, clustering is based on unsupervised learning. Here the dataset you deal with doesn’t have labels, so we cannot use a classification algorithm on a dataset without labels, this is where clustering algorithms comes in. If you want to learn all the clustering algorithms that you should know about as a data scientist, this article is for you. In this article, I will take you through an introduction to all clustering algorithms in machine learning.
Below are all the clustering algorithms that you should know:
- K-Means Clustering
- DBSCAN Clustering
- Agglomerative Clustering
- BIRCH Clustering
- Mean-Shift Clustering
So these are all the clustering algorithms in machine learning you need to know. Now let’s move on to an introduction to all of these clustering algorithms one by one, and their implementation using Python.
K-Means is a clustering algorithm in machine learning that can group an unlabeled dataset very quickly and efficiently in just a few iterations. It works by labelling all instances on the cluster with the closest centroid. When the instances are centered around a particular point, that point is called a centroid. If you receive the instance labels, you can easily locate all items by averaging all instances for each cluster.
But here, we are not given a label or centroids, so we have to start by placing the centroids randomly by selecting k random instances and using their locations as the centroids. Then we label the instances, update the centroids, re-label the instances, update the centroids again and so on. The K-Means clustering algorithm is guaranteed to converge in a few iterations, it will not continue to iterate forever. You can learn about the implementation of the K-Means clustering algorithm from here.
The DBSCAN Clustering algorithm is based on the concept of core samples, non-core samples, and outliers:
- Core Samples: The samples present in the high-density area have minimum sample points with the eps radius.
- Non-core samples: The samples are close to core samples but are not core samples but are very near to the core samples. The no-core samples lie within the eps radius of the core samples but they don’t have minimum samples points.
- Outliers: The samples that are not part of the core samples and the non-core samples and are far away from all the samples.
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 about the implementation of the DBSCAN clustering algorithm from here.
Agglomerative clustering is one of the clustering algorithms where the process of grouping similar instances starts by creating multiple groups where each group contains one entity at the initial stage, then it finds the two most similar groups, merges them, repeats the process until it obtains a single group of the most similar instances.
For example, think of bubbles floating on the water and getting attached, at the end, you will see a large group of bubbles. This is how the agglomerative clustering algorithm works. Some of the advantages of using this algorithm for clustering are:
- It adapts very well to a large number of instances
- It can capture the clusters of different shapes
- It forms flexible and informative clusters
- It can also be used with any pairwise distance
You can learn about the implementation of the Agglomerative clustering algorithm from here.
BIRCH is a clustering algorithm in machine learning that has been specially designed for clustering on a very large dataset. It is often faster than other clustering algorithms like K-Means. It provides a very similar result to the K-Means algorithm if the number of features in the dataset is not more than 20.
When training the model using the BIRCH algorithm, it creates a tree structure with enough data to quickly assign each data point to a cluster. By storing all the data points in the tree, this algorithm allows the use of limited memory while working on a very large dataset. You can learn about the implementation of the BIRCH clustering algorithm from here.
Mean Shift Clustering
Mean Shift clustering is a nonparametric clustering algorithm that does not require any prior knowledge of the number of clusters. Below is the complete process of the Mean Shift clustering algorithm:
- It starts by placing a circle centered on each sample
- Then for each circle, it calculates the mean of all the samples located in the circle
- Then it moves the circle so that it is centered on the mean
- Then it iterates the mean shift step until all of the circles stop moving
- Then it shifts the circles in the direction of the highest density until each circle reaches a maximum of local density
- Then all the instances whose circles have settled in the same place are assigned to the same cluster
Some of the features of this algorithm are like the DBSCAN clustering algorithm, like how it finds any number of clusters of any shape. But unlike the DBSCAN clustering algorithm, the mean shift tends to cut clusters into chunks when they have internal density variations. You can learn about the implementation of the Mean Shift clustering algorithm from here.
So these were all the clustering algorithms in machine learning that you should know. Clustering is the task of identifying similar instances based on similar features and assigning them to clusters based on similar instances. It sounds like classification where each instance is also assigned to a group, but unlike classification, clustering is based on unsupervised learning. I hope you liked this article on all the clustering algorithms that you should know. Feel free to ask your valuable questions in the comments section below.