5 Clustering Methods and Applications

  • Neelam Tyagi
  • Feb 15, 2021
  • Deep Learning
  • Machine Learning
5 Clustering Methods and Applications title banner

In the beginning, let’s have some common terminologies overview, 

 

A cluster is a group of objects that lie under the same class, or in other words, objects with similar properties are grouped in one cluster, and dissimilar objects are collected in another cluster.

 

And, clustering is the process of classifying objects into a number of groups wherein each group, objects are very similar to each other than those objects in other groups. Simply, segmenting groups with similar properties/behaviour and assign them into clusters.

 

Being an important analysis method in machine learning, clustering is used for identifying patterns and structure in labelled and unlabelled datasets. 

 

Clustering is exploratory data analysis techniques that can identify subgroups in data such that data points in each same subgroup (cluster) are very similar to each other and data points in separate clusters have different characteristics.

 

Our main focus of this discussion is “Clustering Methods and Applications”.

 

Before proceeding further, learn more about machine learning algorithms.


 

What are Clustering Methods?

 

  1. Partitioning-based Clustering

 

Partitioning objects into k number of clusters where each partition makes/represents one cluster, these clusters hold certain properties such as each cluster should consist of at least one data object and each data object should be classified to exactly one cluster.

 

These methods are broadly classified to optimize a targeted benchmark similarity function such that distance becomes a significant parameter to consider first. The examples are;

 

  • K-means clustering, (understand K-means clustering from here in detail)
  • CLARANS (Clustering Large Applications based upon Randomized Search)

 

Moreover, Partitioning clustering algorithms are the form of non-hierarchical that generally handle statics sets with the aim of exploring the groups exhibited in data via optimization techniques of the objective function, making the quality of partition better repeatedly.  

 

Partitioning-based clustering is highly efficient in terms of simplicity, proficiency, and easy to deploy, and computes all attainable clusters synchronously.

 

 

  1. Hierarchical-based Clustering

 

Depending upon the hierarchy, these clustering methods create a cluster having a tree-type structure where each newly formed clusters are made using priorly formed clusters, and categorized into two categories: Agglomerative (bottom-up approach) and Divisive (top-down approach). The examples of Hierarchical clustering are

 

  • CURE (Clustering Using Representatives)

  • BIRCH (Balanced Iterative Reducing Clustering and using Hierarchies)

 

The agglomerative clustering method is achieved by locating each point in a cluster, initially and then merging two points closest to it where points represent an individual object or cluster of objects. The divisive clustering first considers the complete population as one cluster and then segments into smaller groups.

 

(Also read: 7 types of Activation Function)

 

 

  1. Density-based Clustering

 

These methods of clustering recognize clusters of dense regions that possess some similarity and are distinct from low dense regions of the space. These methods have sufficient accuracy and the high ability to combine two clusters. Its examples include 

 

  • DBSCAN (Density-based Spatial Clustering of Applications with Noise)

  • OPTICS (Ordering Points to Identify Clustering Structure)

 

These methods implement distance measures between the objects in order to cluster the objects. In most of the cases, clusters, produced using this method, are spherical in shape, so sometimes it becomes hard to identify arbitrary shaped clusters. 

 

Moreover, clusters are produced in all directions as long as the density, residing neighbourhood, surpass some threshold.   

 

Density-based methods save data sets from outliers, the entire density of a point is treated and deciphered for determining features or functions of a dataset that can impact a specific data point. 

 

Some algorithms like OPTICS, DenStream, etc deploy the approach that automatically filtrates noise (outliers) and generates arbitrary shaped clusters.  

 

 

  1. Grid-based Clustering

 

This method follows a grid-like structure, i.e, data space is organized into a finite number of cells to design a grid-structure. Various clustering operations are conducted on such grids (i.e quantized space) and are quickly responsive and do not rely upon the quantity of data objects. Its examples are;

 

  • STING (Statistical Information Grid), 

  • Wave cluster, 

  • CLIQUE (Clustering In Quest)

 

Computing statistical measurements for the grids consequently increasing the speed of method extensively. 

 

Also, the performance of grid-based methods is proportional to the grid-size and demands very less space than the actual data stream.

 

  1. Model-based Clustering

 

These methods deploy a predefined mathematical model for fitting and later on optimizing the data while assuming that the data is hybrid in the form of probability distributions and compute the number of clusters on the basis of standard statistics. 

 

However, the noise and outliers are taken into account while calculating the standard statistics for having robust clustering. In order to form clusters, these clustering methods are classified into two categories: Statistical and Neural Network approach methods. Its examples are;

 

  • MCLUST (Model-based Clustering) 

  • GMM (Gaussian Mixture Models)

 

The model-based algorithms, that use statistical approaches, follow probability measures for determining clusters, and those algorithms that use neural-network approaches, input and output are associated with unit carrying weights.

 

(Most related: Statistical data analysis techniques)


The image is highlighting the clustering algorithms on the basis of clustering methods, like, partitioning-, hierarchical-, density-, grid-, model-based clustering.

Categorization of Clustering Algorithms


Top Clustering Applications

 

Clustering techniques can be used in various areas or fields of real-life examples such as data mining, web cluster engines, academics, bioinformatics, image processing & transformation, and many more and emerged as an effective solution to above-mentioned areas. You can also check machine learning applications in daily life.

 

Some common applications platforms where clustering as a tool can be implemented, are as following;

 

  • Recommendation engines

 

The recommendation system is a widely used method for providing automated personalized suggestions about products, services and information where collaborative filtering is one of the famous recommendation system and techniques

 

In this method, the clustering method provided an idea of like-minded users. The computation/estimation as data provided by several users is leveraged for improving the performance of collaborative filtering methods. And this can be implemented for rendering recommendations in diverse applications. 

 

For example, the recommendation engine is broadly used in Amazon, Flipkart to recommend product and Youtube to suggest songs of the same genre. 

 

Even though dealing with extensive data clustering is suitable as the first step for narrowing the choice of underlying relevant neighbours in collaborative filtering algorithms, that also enhances the performance of complex recommendation engines.

 

Essentially, each cluster will be assigned to specific preferences on the basis of customers’ choices who belong to the cluster. And then, within each cluster, customers would receive recommendations estimated at the cluster level.   

 

  • Market and Customer segmentation

 

A process of splitting the target market into smaller and more defined categories is known as market segmentation. This segments customers/audiences into groups of similar characteristics (needs, location, interests or demographics) where target and personalization, under it, is an immense business. 

 

For instance, a business is looking to get the best return on investment, it is necessary to target customers in a proper way. If wrongs are made then there is a high risk of not making any sales and ruining customers trust.

 

So, the right approach is looking at specific characteristics of people and sharing campaigns with them that are also helpful in engaging with more people of similar behaviour. 

 

Clustering algorithms are capable of grouping people with identical traits and prospects to purchase. For example, once the groups are created, you can conduct a test campaign on each group by sending marketing copy and according to response, you can send more target messages (consisting information about products and services) to them in future. 

 

Under the customer segmentation application, various clusters of customers are made with respect to their particular attributes. On the basis of user-based analysis, a company can identify potential customers for their products or services.

 

As groups of identical customers are made by clustering method in this area, it is very similar to collaborative filtering while embracing the very fine difference, here, irregular characteristics of objects are deployed for clustering purposes rather than rating/review information.

 

Clustering methods enable us to segment customers into diverse clusters, depending on which companies can consider novel strategies to apply to their customer base.

 

For example, K-means clustering is helpful for marketers to improve customer base, work on targeted areas, and divide customers on the basis of purchase history, interests or activities.

 

Another example, a telecom company makes a cluster of prepaid users to understand the pattern/behaviour in the form of recharging amount, sending SMS, and using the internet, this also helps a company to make segments and plan any campaigns for targeted users (specific cluster of users).  

 

  • Social Network Analysis (SNA)

 

It is the process of examining qualitative and quantitative social structures by utilizing Graph Theory (a major branch of discrete mathematics) and networks. Here the mapping of social networks structure is arranged in terms of nodes (individual personality, people, or other entity inside the network) and the edges or links (relationships, interaction, or communication) that connect them. 

 

(Recommend Read: What is the knowledge graph?)

 

Clustering methods are required in such analysis in order to map and measure the relationship and conflicts amid people, groups, companies, computer networks, and other similar connected information/knowledge entities. 


The image is showing the social network structure (nodes-edges structure) for mapping the relationship amid, actors, people, groups, companies, etc.

Nodes and connections in Social Network Analysis (SNA)


Clustering analysis can provide a visual and mathematical analysis/presentation of such relationships and give social network summarization.

 

For example, for understanding a network and its participants, there is a need to evaluate the location and grouping of actors in the network, where the actors can be individual, professional groups, departments, organizations or any huge system-level unit.

 

Now, through a clustering approach, SNA can visualize the interaction among participants and obtain insights about several roles and groupings in the network, such as who are connectors, bridges,  and experts, who are isolated actors and much similar information. It also tells where there are clusters, who are into them, who are at the gist in the network or on the outer edge.

 

  • Search Result Clustering 

 

You must have encountered similar results obtained while searching something particular at Google, these results are a mixture of the similar matches of your original query. 

 

Basically, this is the result of clustering, it makes groups of similar objects in a single cluster and renders to you, i.e provides results of searched data in terms with most closely related objects that are clustered across the data to be searched. 

 

Better the clustering algorithm deployed, more the possibilities of achieving required outcomes of the leading desk. 

 

Therefore, the concept of similar objects serves as a backbone in getting searched results. Even though, most of the parameters are taken into consideration for defining the portrait of similar objects.

 

Check the image below while typing “search engine” at Google, we get the more keywords of a similar search, for example, search engine list, top 50 search engine, etc.


The image is displaying various similar keywords while searching for a "search engine".

Google search engine, source: Google


Depending on the closest similar objects/properties, the data is assigned to a single cluster,  giving the plethora sets of similar results of the users. In simple terms, the search engine attempts to group identical objects in one cluster and non-identical objects in another cluster. 

 

  • Biological Data Analysis, Medical Imaging Analysis and Identification of Cancer Cells

 

One of the means to connect analytical tools with biological content is Biological data analysis for a heavy and extended understanding of the relationships identified as to be linked with experimental observations.

 

Moreover, biological data is structured either in the form of networks or sequences where clustering methods are significant for identifying profound similarities.

 

On the other side, from the past few years, the exploitation of research done on the human genome and the expanding facility of accumulating diverse types of gene expression data lead to evolving biological data analysis exponentially.

 

Clustering helps in extracting useful knowledge from huge datasets collected in biology, and other life sciences realm as medicine or neuroscience with the fundamental aim of providing prediction and description of data structure.

 

Using clustering algorithms, cancerous datasets can be identified, a mix datasets involving both cancerous and non-cancerous data can be analyzed using clustering algorithms to understand the different traits present in the dataset, depending upon algorithms produces resulting clusters.

 

On feeding to unsupervised clustering algorithms, we obtain accurate results from cancerous datasets. 


 

Key Takeaways

 

  1. A cluster is the data objects of similar traits under one group.

  2. Under the clustering process, groups are made of abstracted objects into classes of similar objects. 

  3. Under clustering analysis, the first set of objects are categorized into groups based on similarity and then assign labels to the groups.

  4. Partitioning based, hierarchical based, density-based-, grid-based-, and model-based clustering are the clustering methods.

  5. Clustering technique is used in various applications such as market research and customer segmentation, biological data and medical imaging, search result clustering, recommendation engine, pattern recognition, social network analysis, image processing, etc.

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