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

Imagine you’re in a conversation with your organization’s Chief Marketing Officer. The company aims to gain deeper insights into its customers using data to drive business objectives and enhance customer experiences. This is where clustering, or grouping similar data points, comes into play. Clustering facilitates the organization of data into understandable structures. Particularly with the influx of big data, clustering is invaluable. It not only structures data but also aids in informed decision-making. Essentially, clustering groups diverse data types together based on common factors and parameters. Wondering, “What is clustering?” and different types of clustering methods are available, each designed to expedite and simplify the process, let’s delve deeper into what clustering entails. 

In this article, I’ll explain clustering and its types. Let’s explore the different methods of clustering below. 

1. Density-Based Clustering
2. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
3. OPTICS (Ordering Points to Identify Clustering Structure)
4. HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise)
5. Hierarchical Clustering
6. Fuzzy Clustering
7. Partitioning Clustering
8. PAM (Partitioning Around Medoids)
9. Grid-Based Clustering

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What is Clustering in Data Mining/Machine Learning?

Clustering is a type of unsupervised learning method 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 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 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.

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What is clustering and its types?

Clustering is a technique used in machine learning to group similar objects into sets called clusters. The main types of clustering algorithms are K-means, Hierarchical, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Each type has distinct methods for grouping data based on similarity measures.

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. 

What are the types of Clustering Methods?

Clustering 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 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. 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.

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

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 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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Fuzzy 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 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

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 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.

Applications of Clustering in Different Fields 

Let us understand some applications of clustering in various fields: 

• Marketing and Customer Segmentation: 

Clustering, as far as marketing is concerned, plays a very crucial role in customer segmentation so that business organizations will be able to know the different needs of customers. Through segmentation, by which customers are grouped according to common characteristics, marketing strategies can be customized for particular segments of the market.  

The result is more efficient communication, individual promotion campaigns, and satisfied customers. For example, an e-commerce platform can segment customers according to their previous transactions and, therefore deliver advertisements that are targeted or improve the experience of shopping. 

• Healthcare: 

What does clustering mean in healthcare? We know that healthcare is essential in-patient stratification, especially regarding healthcare. Classifying patients based on similar medical profiles helps healthcare providers provide personalized treatment plans for cases, predict outcomes of diseases, and identify appropriate clinical trial candidates. 

 His use of clustering addresses the idea behind personalized medicine—treatments that are implemented with respect to specific characteristics and features of a patient, which leads to enhancing care for patients as well as improving outcomes. 

•  Image and Pattern Recognition: 

Clustering plays a fundamental role in image analysis and pattern recognition. In the field of image processing, it tends to associate similar images together which makes it relevant in various applications such as object recognition and segmentation etc.  

 The utility of pattern recognition can be seen in handwriting, speech, and computer vision, whereby it is possible to discover similar patterns from which a unique algorithm gets the prospect that if such kinds derived have successful outcomes, then indeed, should help solve classification problems at hand. 

•  Anomaly Detection in Cybersecurity: 

Clustering is an essential element of anomaly detection to carry out a cyber security initiative. Through the development of groups with expected patterns, any indication outside these characteristics can thus raise the alarm as a possible cyber threat. This step alerts organizations to ensure they don’t suffer from security breaches that could interfere with secretive data and the integrity of digital systems. 

•  Document Classification and Information Retrieval: 

The use of text clustering is widespread in document classification and information retrieval. Clustering mainly groups together documents with the same or similar content – as is desirable in text mining for subsequent organization and retrieval purposes. This application has applications in information retrieval systems that enable users to find the most appropriate document and associated tasks, including document summarization and sentiment analysis. 

•  Social Network Analysis: 

Cluster computing is the supporting technique of social network analysis, which helps communities or groups catch people to bind together due to similarity. Such information is beneficial for marketing purposes, content recommendation, and understanding the dynamics of social networks. By allowing for the clustering of social media sites, platforms are able to generate individualized benefits that limit those already and be given, fostering better engagement and overall experiences while using networking. 

End Notes

I’ve provided an overview of clustering and its different methods in this article, showcasing some practical examples. It’s a helpful starting point for anyone looking to understand clustering better. 

Each clustering method has strengths and limitations, making them suitable for specific types of data sets. Factors like hardware specifications and algorithm complexity also influence data analysis outcomes. 

As an analyst, I’ve learned that selecting the correct algorithm for a given scenario is crucial. There’s no one-size-fits-all solution in machine learning. That’s why it’s essential to experiment and immerse yourself in the clustering world. 

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