Clustering in Machine Learning Explained With Examples

In machine learning, we use different machine learning techniques to analyze data. We can use different approaches like regression, classification, and clustering to analyze the data based on the type of data and the problem statement. In this article, we will discuss clustering and its examples in machine learning. We will also discuss various types of clustering algorithms in machine learning.

What is Clustering in Machine Learning?

Clustering is an unsupervised machine learning algorithm. In clustering, we group data into small clusters based on their features. The grouping works on the principle that the data in a single cluster have maximum similarity and the data between two different clusters have maximum dissimilarity. Clustering imitates the ability of humans to differentiate between objects based on their features. 

For instance, if we are given a basket containing apples and tomatoes, we can easily divide the items into different clusters containing tomatoes and apples each. In this process, we don’t need to be told that a particular item is an apple or a tomato. We can simply distinguish the physical features such as the shape and color of the items and group them based on their similarity. We don’t even need to know which item is a tomato or apple. 

Clustering algorithms in machine learning work in a similar fashion. If given a dataset, the clustering algorithms group the data entries into various clusters based on the values of the attributes in the dataset. We call clustering algorithms unsupervised because we don’t need to specify how the algorithm should divide the data into different clusters. The algorithm compares the data entries in the dataset and divides them into clusters such that the data in a single cluster have maximum similarity and data in two different clusters have maximum dissimilarity. 

Need For Clustering in Machine Learning

Clustering is used in various applications where other machine learning techniques don’t work. For instance, if you need to classify an unlabeled dataset into different classes, the only way possible to classify the dataset is to first use clustering algorithms to group the data. After clustering, we can label the data into different classes.

While data preprocessing, we often use clustering algorithms to find outliers. Finding an outlier is an important step in data cleaning. If you don’t remove outliers from the dataset, it is possible that your machine learning model will be inaccurate. In the case of highly sensitive algorithms like polynomial regression, your machine learning model will become highly inaccurate if you don’t remove outliers. Thus, clustering finds its application in even those processes where we don’t need to group data into clusters. 

Different Types of Clustering Algorithms

We use various clustering algorithms based on the business requirements and the features of the available dataset. Following are the different types of clustering algorithms that you should have knowledge about.

Partition Based Clustering

In partition-based clustering, the clustering algorithm groups the data into a finite number (K) of groups. We need to specify the number of clusters beforehand. While clustering, the machine learning model chooses K number of centroids and the dataset is clustered into k groups according to the distance from the centroid. The K-means clustering algorithm works using the partitioning clustering technique.  

In K-means clustering, we initially select K centroids or data points. Then, each data point is assigned to one of the centroids based on their distance from the centroid. In the next iterations, the centroid and the related cluster’s data points are updated. The adjustments are made in order to ensure that the data point’s distance from the current cluster’s center is the least compared to that of other centroids.

Suggested Article: K-Means Clustering Explained with Numerical Example

Hierarchy Based Clustering

Hierarchy-based clustering, as the name suggests, is used to group data in a dataset into hierarchies. Here, we don’t need to specify the number of clusters that need to be formed. In hierarchy-based clustering, the dataset is divided hierarchically to form tree-like structures called dendrograms. 

While creating the hierarchical structure, you can start with the entire dataset and follow the divisive approach to partition the data into different hierarchy levels. The divisive approach is a top-down approach in which we start by considering the dataset a single cluster and keep partitioning them into different clusters. You can also start with the bottom-up approach. In the bottom-up approach, we start by considering each data point a single cluster. After that, we merge the clusters based on their similarities to form hierarchies. 

Agglomerative clustering is a prime example of hierarchical clustering that uses a bottom-up approach for clustering.

Density-Based Clustering

In density-based clustering, we analyze the distribution of the data points in the data space. Here, the machine learning model identifies the dense areas with data points having high similarity to each other and assigns them into a cluster. Each cluster is separated from other clusters by sparse areas in the data space. This clustering technique uses parameters like data reachability and data connectivity to determine clusters. The density-based clustering algorithms are best suited for evaluating and finding clusters having non-linear shapes based on the density of the data points.

The DBSCAN clustering algorithm is one of the most popular examples of density-based clustering algorithms. Here, DBSCAN is an acronym for Density-Based Spatial Clustering of Applications with Noise. 

Model-Based Clustering

In model-based clustering, we use a statistical approach to determine the clusters in the dataset. This approach works on the principle that “The entire dataset is generated from a finite mixture of component models where each component model is a probability distribution, generally a parametric multivariate distribution .”

For instance, consider this example. In a multivariate Gaussian mixture model, each component will be a multivariate Gaussian distribution. Here, the cluster to which a particular data point belongs is determined by the component responsible for generating the particular data point.

To rephrase, each data point in model-based clustering is considered to be created from a distribution of a mixture of cluster components. Here, each cluster component has a density function that determines the probability or weight of the component in the mixture.

Fuzzy Clustering

Fuzzy clustering is a clustering technique where we don’t define strict boundaries between the clusters. In fuzzy clustering, a single data point can belong to two or more clusters at once. An example of fuzzy clustering can be the clustering of apples into red and green apples.

While clustering, we can either assign an apple to be green or red. However, it is possible that the apple can be green to some extent and also red to some extent. In this case, we use fuzzy clustering and assign a probability for the apple to be green or red. Thus, an apple can be clustered into green and red at the same time according to the probability of it being green or red. 

By now, we have discussed the basics of clustering and its types in machine learning. Let us now discuss different applications of clustering in machine learning applications in the real world.

Examples of Clustering in Machine Learning Applications

• We can use clustering algorithms to identify cancer cells from a dataset of images of cancerous and non-cancerous cells. The clustering algorithms can compare the features of the images and they can easily identify patterns to cluster the images into clusters containing cancerous and non-cancerous cells.
• The e-commerce websites and retail shopping chains store the transaction data of each customer. They perform clustering on this data to identify customers with similar interests. This helps them recommend products to the customers resulting in more sales. For instance, when you buy an item on an e-commerce website, it shows you suggestions saying that the people who bought the particular item bought these items too. Well, this feature is facilitated by using clustering and association rule mining to help the websites sell more. 
• Clustering is also used in grouping different plant and animal species using various features of the animals. You can also derive plant and animal taxonomies and classify genes using clustering to gain insights into structures inherent to a given population.
• We use clustering for classifying documents for information discovery. The search engines also use clustering to provide you with search results based on your query.
• Banks and credit card companies use clustering to identify credit and insurance frauds. Normally, the frauds are identified because they don’t fit into a cluster and are considered outliers due to their features.
• Social media platforms use clustering to suggest new connections to you based on your existing connections. The post recommendation of these platforms is also facilitated by clustering algorithms. This helps the social media platforms serve you the content of your interest to increase customer engagement.

Suggested Reading:Data Modeling Tools You Must Try in 2022

Conclusion

In this article, we have discussed the basics of clustering in machine learning. We also discussed the types of clustering algorithms along with some examples of clustering. I hope you enjoyed reading this article. To learn more about machine learning, you can read this article on regression in machine learning. You might also like this article on machine learning tools.

For other programming topics, you can read this article on dynamic role-based authorization using ASP.net. You can also read this article on user activity logging using Asp.net.