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What is Clustering in Machine Learning and Different Types of Clustering Methods

What is Clustering in Machine Learning and Different Types of Clustering Methods

Q: 1: What is the main purpose of clustering in machine learning?

Clustering in machine learning is used to group similar data points together based on their characteristics. It helps uncover patterns and insights in datasets without requiring labeled data, making it useful for tasks like customer segmentation, anomaly detection, and market analysis.

Q: 2: How does hierarchical clustering differ from other clustering methods?

Hierarchical clustering, one of the types of clustering methods creates a tree-like structure (dendrogram) to represent data clusters, either by starting with individual data points and merging them (agglomerative) or by starting with all points in one cluster and dividing them (divisive). It is useful for understanding nested relationships within data.

Q: 3: What is the advantage of using density-based clustering algorithms like DBSCAN?

Density-based clustering in machine learning, like DBSCAN, groups data points based on regions of high density. It can find clusters of arbitrary shape and is effective at handling noise and outliers, making it ideal for datasets with varying densities.

Q: 4: Can clustering be applied to time-series data?

Yes, clustering can be applied to time-series data to group similar time-dependent patterns. Techniques like K-Means and DBSCAN can be adapted to identify trends, seasonal patterns, or anomalies in time-series datasets.

Q: 5: How does the K-Means algorithm work in clustering in Machine Learning?

In K-Means clustering, one of the types of clustering methods, the data is partitioned into K clusters based on the nearest centroids. The algorithm iteratively reassigns data points to the closest centroid and recalculates centroids until the clusters stabilize, minimizing the within-cluster variance.

Q: 6: What is the difference between hard clustering and soft clustering?

In hard clustering, each data point belongs to one and only one cluster, while in soft clustering, data points can belong to multiple clusters with different degrees of membership, represented by probabilities or membership values.

Q: 7: How does fuzzy clustering differ from K-Means clustering?

Fuzzy clustering allows each data point to belong to multiple clusters with varying degrees of membership, while K-Means assigns each data point to exactly one cluster. Fuzzy clustering is useful for overlapping clusters where boundaries are not well-defined.

Q: 8: What are the key applications of clustering in healthcare?

In healthcare, clustering helps in patient stratification by grouping patients with similar medical profiles. It enables personalized treatment plans, predicts disease outcomes, and identifies suitable candidates for clinical trials, improving patient care and outcomes.

Q: 9: Can clustering in Machine Learning be used for anomaly detection in cybersecurity?

Yes, clustering is widely used in anomaly detection for cybersecurity. By grouping data points based on expected behavior, clustering helps identify unusual patterns or outliers, signaling potential cyber threats and enabling timely intervention.

Q: 10: How does dimensionality reduction relate to clustering?

Clustering can benefit from dimensionality reduction techniques like PCA (Principal Component Analysis) by reducing the number of features in high-dimensional data. This makes clustering algorithms more efficient and effective at detecting meaningful patterns in the data.

By Rohit Sharma

Updated on May 15, 2025 | 16 min read | 126.22K+ views

Table of Contents

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What is Clustering in Machine Learning/Data Mining?
What is Clustering in AI & Why Clustering?
What are the types of Clustering Methods?
Applications of Clustering in Different Fields
Become an Expert at Clustering in Machine Learning with upGrad!

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Clustering in Machine Learning is an unsupervised learning technique that groups data points into clusters based on their similarities. Unlike supervised learning, where the model is trained with labeled data, clustering discovers hidden patterns in data, making it ideal for tasks like customer segmentation, recommendation systems, and anomaly detection.

In this blog, we’ll explore key clustering methods like K-Means, Hierarchical, and DBSCAN, highlighting their practical applications.

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What is Clustering in Machine Learning/Data Mining?

Clustering is a type of unsupervised learning algorithm of machine learning. In the unsupervised learning method, the inferences are drawn from the data sets which do not contain labelled output variable. It is an exploratory data analysis technique that allows us to analyze the multivariate data sets.

Clustering is a task of dividing the data sets into a certain number of clusters in such a manner that the data points belonging to a cluster have similar characteristics. Clusters are nothing but the grouping of data points such that the distance between the data points within the clusters is minimal. Clustering in Machine Learning is done to segregate the groups with similar traits.

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In other words, the clusters are regions where the density of similar data points is high. It is generally used for the analysis of the data set, to find insightful data among huge data sets and draw inferences from it. Generally, the clusters are seen in a spherical shape, but it is not necessary as the clusters can be of any shape. Learn about clustering in Machine Learning and more data science concepts in our data science online course.

It depends on the type of algorithm we use which decides how the clusters will be created. The inferences that need to be drawn from the data sets also depend upon the user as there is no criterion for good clustering.

Also Read: What is Cluster Analysis in Data Mining? Methods, Benefits, and More

What is Clustering in AI & Why Clustering?

We have explored what is clustering, and now we have to see why we should prefer clustering. Let us understand some of them:

Pattern Recognition: Clustering is a method that allows Artificial Neural Networks (ANN) to detect complex patterns from data through grouping similar instances. This is especially timely in applications where it plays a significant role in identifying relationships and structures within the dataset, enabling this network to generalize from these patterns.
Dimensionality Reduction Clustering helps in the dimensionality reduction of high-dimensional data. It assists in identifying pertinent characteristics and acts to eliminate problems accompanied by the curse of dimensionality. The neural network becomes efficient in computation because it will concentrate only on important information.
Data Understanding: Cluster computing helps group objects based on similarity index. Grouping helps to see patterns and subtleties of distribution analytically. This insight is important because it informs the modelling of neural network architectures and helps developers establish suitable training regimes that can be used to match particular data with a model.
Feature Selection: Clustering helps find the solid features for all individuals on a corresponding cluster. In these regards, this approach is practical in feature selection, where it helps the neural network to concentrate on significant aspects of data during training, hence improving model performance.

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Also Read: Clustering vs Classification: What is Clustering & Classification

What are the types of Clustering Methods?

Clustering in machine learning itself can be categorized into two types viz. Hard Clustering and Soft Clustering. In hard clustering, one data point can belong to one cluster only. But in soft clustering, the output provided is a probability likelihood of a data point belonging to each of the pre-defined numbers of clusters.

Density-Based Clustering in Machine Learning

In this method, the clusters are created based upon the density of the data points which are represented in the data space. The regions that become dense due to the huge number of data points residing in that region are considered as clusters.

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The data points in the sparse region (the region where the data points are very less) are considered as noise or outliers. The clusters created in these methods can be of arbitrary shape. This approach is one of the types of clustering methods that focuses on density to identify meaningful groups, distinguishing it from other techniques like partitioning or hierarchical clustering. Following are the examples of Density-based clustering algorithms:

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DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

DBSCAN groups data points together based on the distance metric. It follows the criterion for a minimum number of data points. It can discover clusters of different shapes and sizes from a large amount of data, which is containing noise and outliers.It takes two parameters – eps and minimum points. Eps indicates how close the data points should be to be considered as neighbors. The criterion for minimum points should be completed to consider that region as a dense region.

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OPTICS (Ordering Points to Identify Clustering Structure)

OPTICS follows a similar process as DBSCAN but overcomes one of its drawbacks, i.e. inability to form clusters from data of arbitrary density. It considers two more parameters which are core distance and reachability distance. Core distance indicates whether the data point being considered is core or not by setting a minimum value for it.

Reachability distance is the maximum of core distance and the value of distance metric that is used for calculating the distance among two data points. One thing to consider about reachability distance is that its value remains not defined if one of the data points is a core point.

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HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise)

HDBSCAN is a density-based clustering method that extends the DBSCAN methodology by converting it to a hierarchical clustering algorithm.

Hierarchical Clustering in Machine Learning

Hierarchical Clustering groups (Agglomerative or also called as Bottom-Up Approach) or divides (Divisive or also called as Top-Down Approach) the clusters based on the distance metrics.

In agglomerative clustering, initially, each data point acts as a cluster, and then it groups the clusters one by one. This comes under in one of the most sought-after clustering methods.

Divisive is the opposite of Agglomerative, it starts off with all the points into one cluster and divides them to create more clusters. These algorithms create a distance matrix of all the existing clusters and perform the linkage between the clusters depending on the criteria of the linkage. The clustering of the data points is represented by using a dendrogram. There are different types of clustering methods linkages: –

o Single Linkage: – In single linkage the distance between the two clusters is the shortest distance between points in those two clusters.

o Complete Linkage: – In complete linkage, the distance between the two clusters is the farthest distance between points in those two clusters.

o Average Linkage: – In average linkage the distance between the two clusters is the average distance of every point in the cluster with every point in another cluster.

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Read: Common Examples of Data Mining.

Fuzzy Clustering in Machine Clustering

In fuzzy clustering, the assignment of the data points in any of the clusters is not decisive. Here, one data point can belong to more than one cluster. It provides the outcome as the probability of the data point belonging to each of the clusters. One of the algorithms used in fuzzy clustering is Fuzzy c-means clustering.

This algorithm is similar in approach to the K-Means clustering. It differs in the parameters involved in the computation, like fuzzifier and membership values. In this type of clustering method, each data point can belong to more than one cluster. This clustering in machine learning technique allocates membership values to each image point correlated to each cluster center based on the distance between the cluster center and the image point.

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Partitioning Clustering in Machine Learning

This method is one of the most popular choices for analysts to create clusters. In partitioning clustering, the clusters are partitioned based upon the characteristics of the data points. We need to specify the number of clusters to be created for this clustering method. These clustering algorithms follow an iterative process to reassign the data points between clusters based upon the distance. The algorithms that fall into this category are as follows: –

o K-Means Clustering: – K-Means clustering is one of the most widely used algorithms. It partitions the data points into k clusters based upon the distance metric used for the clustering. The value of ‘k’ is to be defined by the user. The distance is calculated between the data points and the centroids of the clusters.

K-means clustering is a type of unsupervised learning used when you have unlabeled data (i.e., data without defined categories or groups). This algorithm aims to find groups in the data, with the number of groups represented by the variable K. In this clustering method, the number of clusters found from the data is denoted by the letter ‘K.’

The data point which is closest to the centroid of the cluster gets assigned to that cluster. After an iteration, it computes the centroids of those clusters again and the process continues until a pre-defined number of iterations are completed or when the centroids of the clusters do not change after an iteration.

It is a very computationally expensive algorithm as it computes the distance of every data point with the centroids of all the clusters at each iteration. This makes it difficult for implementing the same for huge data sets.

PAM (Partitioning Around Medoids)

This algorithm is also called as k-medoid algorithm. It is also similar in process to the K-means clustering algorithm with the difference being in the assignment of the center of the cluster. In PAM, the medoid of the cluster has to be an input data point while this is not true for K-means clustering as the average of all the data points in a cluster may not belong to an input data point.

o CLARA (Clustering Large Applications): – CLARA is an extension to the PAM algorithm where the computation time has been reduced to make it perform better for large data sets.

It arbitrarily selects a portion of data from the whole data set, as a representative of the actual data. It applies the PAM algorithm to multiple samples of the data and chooses the best clusters from a number of iterations. It uses only random samples of the input data (instead of the entire dataset) and computes the best medoids in those samples. It works better than K-Medoids for crowded datasets. It is intended to reduce the computation time in the case of a large data set.

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Grid-Based Clustering in Machine Learning

In grid-based clustering, the data set is represented into a grid structure which comprises of grids (also called cells). The overall approach in the algorithms of this method differs from the rest of the algorithms.

They are more concerned with the value space surrounding the data points rather than the data points themselves. One of the greatest advantages of these algorithms is its reduction in computational complexity. This makes it appropriate for dealing with humongous data sets.

After partitioning the data sets into cells, it computes the density of the cells which helps in identifying the clusters. A few algorithms based on grid-based clustering are as follows: –

o STING (Statistical Information Grid Approach): – In STING, the data set is divided recursively in a hierarchical manner. Each cell is further sub-divided into a different number of cells. It captures the statistical measures of the cells which helps in answering the queries in a small amount of time. Each cell is divided into a different number of cells. Thereafter, the statistical measures of the cell are collected, which helps answer the query as quickly as possible.

o WaveCluster: – In this algorithm, the data space is represented in form of wavelets. The data space composes an n-dimensional signal which helps in identifying the clusters. The parts of the signal with a lower frequency and high amplitude indicate that the data points are concentrated. These regions are identified as clusters by the algorithm. The parts of the signal where the frequency high represents the boundaries of the clusters. It could use a wavelet transformation to change the original feature space to find dense domains in the transformed space. For more details, you can refer to this paper.

o CLIQUE (Clustering in Quest): – CLIQUE is a combination of density-based and grid-based clustering algorithm. It partitions the data space and identifies the sub-spaces using the Apriori principle. It identifies the clusters by calculating the densities of the cells. It can find clusters of any shape and is able to find any number of clusters in any number of dimensions, where the number is not predetermined by a parameter. It outperforms K-means, DBSCAN, and Farthest First in both execution, time, and accuracy.

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Applications of Clustering in Different Fields

Clustering in machine learning is a powerful technique in machine learning that is being used across various industries to analyze and organize data, identify patterns, and make informed decisions. Let’s explore how clustering is applied in different fields with real-world examples:

1. Marketing and Customer Segmentation

Clustering plays a crucial role in customer segmentation, enabling businesses to understand their customers' needs and behaviors. By grouping customers with similar characteristics, companies can create tailored marketing strategies, leading to more personalized communication, targeted promotions, and improved customer satisfaction.

Example:
Amazon uses clustering to segment customers based on past purchases and browsing history, allowing it to recommend products effectively. Similarly, Netflix groups users with similar viewing preferences to suggest personalized content, enhancing user experience and retention.

2. Healthcare: Personalized Medicine

In healthcare, clustering is used to classify patients based on their medical profiles, helping healthcare providers offer personalized treatment plans. This leads to better patient outcomes by tailoring healthcare interventions according to the specific needs of each individual.

Example:
Kaiser Permanente uses clustering to group patients with similar conditions, improving care management. This approach aids in predicting disease outcomes and identifying clinical trial candidates, ensuring patients receive the most effective treatments based on their unique medical history.

3. Image and Pattern Recognition

Clustering in machine learning is fundamental to image processing and pattern recognition. By grouping similar images, it helps in object recognition, facial detection, and even handwriting recognition.

Example:
Google Photos uses clustering to automatically organize images based on their content, such as grouping photos of the same person or event. Face++, a facial recognition service, uses clustering for accurate identification of faces in images, applicable in security systems and identity verification.

4. Anomaly Detection in Cybersecurity

In cybersecurity, clustering helps detect anomalies by grouping normal patterns of behavior and flagging deviations that may indicate a potential security threat, such as fraud or a data breach.

Example:
PayPal employs clustering to identify fraudulent transactions by analyzing transaction patterns. When a transaction differs significantly from typical patterns, it’s flagged for review. Similarly, Darktrace uses clustering to detect network anomalies, alerting organizations to potential cyber threats before they escalate.

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5. Document Classification and Information Retrieval

Clustering is widely used in text mining for document classification and information retrieval. By grouping similar documents, it makes searching, summarizing, and analyzing large volumes of data more efficient.

Example:
Google Search clusters results into categories like news, images, and videos, allowing users to quickly find what they need. IBM Watson uses clustering for sentiment analysis, grouping similar text data to help businesses understand customer feedback and improve services.

6. Social Network Analysis

Clustering is crucial in social network analysis, where it helps identify communities and understand the dynamics of social groups. This has major applications in marketing, content recommendation, and understanding social behavior.

Example:
Facebook clusters users based on similar interests, suggesting friends and groups. Twitter uses clustering to identify trending topics and recommend accounts to follow, enhancing user engagement and content discovery. This process also helps marketers target ads effectively and improve user experiences.

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Become an Expert at Clustering in Machine Learning with upGrad!

Clustering is an essential technique in machine learning that enables you to group similar data points and reveal hidden patterns. Methods like K-Means, DBSCAN, and Hierarchical clustering are widely used in fields such as marketing, healthcare, and cybersecurity to drive actionable insights and decision-making. Mastering these techniques allows you to analyze complex datasets with greater precision.

To further sharpen your skills and advance your career, upGrad’s AI and ML courses provide expert-led guidance and hands-on experience. These courses focus on practical applications, helping you bridge skill gaps and confidently apply clustering methods to real-world projects.

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