Cluster Analysis: Types and Applications

Cluster Analysis 

 

Data science is a vast field that is operational in almost every industrial sector today. With loads of data flooding organizations, it is important to organize it and keep substantial records of it. 

 

That said, statistics help a lot in achieving this purpose. Furthermore, data analytics has become a major operation in the field of data science as it helps to derive patterns from vast amounts of data for making future decisions for the betterment of an organization. 

 

A number of techniques under this concept are incorporated to organize data and make it more approachable. Among a variety of techniques, cluster analysis is one such statistical method. 

 

Cluster analysis in statistics is a method to organize data by clustering data points in a particular cluster. Rightly put, cluster analysis is a way of putting data points with similar characteristics in one group so that they differ from other data points of other clusters. 

 

It must be noted that the level of similarity between two data points in one cluster is maximum while the similarity between two data points of different clusters is minimum. 

 

With the help of a set of algorithms empowered by Machine learning, cluster analysis discriminates among various data points in such a way that those with maximum differences are placed in different clusters. 

 

"Researchers are often confronted with the task of sorting observed data into meaningful structures. Cluster analysis is an inductive exploratory technique in the sense that it uncovers structures without explaining the reasons for their existence."

Purpose of Cluster Analysis 

 

Cluster Analysis in Machine Learning 

 

When it comes to Machine learning algorithms, clustering in machine learning is a prominent statistical technique that has evolved over time. 

 

A set of clustering algorithms, empowered by the duo of cluster analysis and machine learning, have emerged on the surface that enables organizations and industries to run through their data and cluster data in an organized manner. 

 

Clustering is one of the most renowned unsupervised machine learning algorithms that has been known to humankind. 

 

(Also read - Top ML Techniques)

 

Broadly, there are 6 types of clustering algorithms in Machine learning. They are as follows - centroid-based, density-based, distribution-based, hierarchical, constraint-based, and fuzzy clustering. 

 

"Homogeneity plays a crucial role in clustering as the algorithms learn to identify similar grounds in datasets that are provided to the machines."

Learning about Clustering Algorithms 

 

 

Types of Cluster Analysis 

 

Broadly, there are 2 types of cluster analysis methods. On the basis of the categorization of data sets into a particular cluster, cluster analysis can be divided into 2 types - hard and soft clustering. They are as follows -

 

1. Hard Clustering

 

In a given dataset, it is possible for a data researcher to organize clusters in a manner that a single dataset is placed in only one of the total number of given clusters. 

 

This implies that a hard-core classification of datasets is required in order to organize and classify data accordingly. 

 

For instance, a clustering algorithm classifies data points in one cluster such that they have the maximum similarity. However, there are no other grounds of similarity with data sets belonging to other clusters.

 

 

2. Soft Clustering

 

The second class of cluster analysis is Soft Clustering. Unlike hard clustering that requires a given data point to belong to only a cluster at a time, soft clustering follows a different rule. 

 

In the case of soft clustering, a given data point can belong to more than one cluster at a time. This means that a fuzzy classification of datasets characterizes soft clustering. 

 

Fuzzy Clustering Algorithm in Machine learning is a renowned unsupervised algorithm for processing data into soft clusters. 

 

(Related blog: A Fuzzy-Logic Approach In Decision-Making)

 

Let us understand the types of cluster analysis with the help of a common cluster analysis example. Herein, we shall consider a group of high-school students in the West as a group of data points in a given dataset. 

 

In order to cluster students on the basis of the subjects they study, the administration will have to figure out the maximum similarity between these students. Now, in order to do so, the administration department will have to identify common classes of 20 students in a batch. 

 

However, since many subjects are studied by different groups of students, they can only be clustered on the basis of soft clustering. This is because a student can also be placed in the cluster of Math subject and English subject at the same time. 

 

Therefore, this example clearly justifies the purpose of soft clustering. On the other hand, when we consider an example of students studying in the East, a group of students is clustered on the basis of hard clustering. This implies that they are placed in only one cluster at a time wherein they study all subjects along with their fellow classmates. 

 

Applications of Cluster Analysis

 

As we have read about cluster analysis, this segment will introduce us to the real-world use of cluster analysis. 

 

On paper, the concept seems interesting. However, now we will discover how it is used in various industries. Here is a brief list of the applications of cluster analysis. 

 

1. Data Science

 

The largest application of cluster analysis in the real world, data science uses clustering analysis on a vast scale. For organizing data and classifying data points in different clusters, cluster analysis is important for analyzing data in both quantitative and qualitative manners.

 

Especially when it comes to cluster analysis in data mining, the former plays a more important role in segregating data points and organizing them on the basis of their similarity. 

 

(Suggested blog: Applications of data science)

 

 

2. Marketing

 

Marketing is the act of promoting goods or commodities in order to increase reachability and popularity among target customers. With the help of cluster analysis, marketing specialists can easily segregate the market and organize their target audience for better reach and marketing. 

 

Moreover, clustering also helps in classifying products on the basis of their homogeneity in order to achieve an organized sense of goods traded to customers on a large scale. 

 

 

3. Social Network Analysis

 

Social Network Analysis is a concept that allows data scientists to study social structures and their basis of formations. When combined with cluster analysis, SNA can classify data points and cluster them with a better insight. 

 

In order to get a better understanding of how social structures are made on the grassroots level, social network analysis software with in-built clustering ability can lead to enhanced performance and qualitative results in the long run. 

 

That said, clustering can also help to detect anomalies in a given social structure and thereby assist in digging out causes for the same. 

 

 

4. Image Segmentation

 

Image Segmentation has risen to popularity in recent times with the increasing demand for dismantling an image into various fragments. 

 

Clustering in image segmentation refers to the segregation of homologous characteristics in an image and clustering such data points together. 

 

“It creates a pixel-wise mask for the objects in an image which helps models to understand the shape of objects and their position in the image at a more granular level."

Image Segmentation using Cluster Analysis 

 

 

5. Collaborative Filtering

 

With the increasing use of digital media, recommender systems have emerged that use the technique of collaborative filtering for recommending users or items based on a user's past interactive data. 

 

This is a popular application of clustering. How? Well, in order to classify items or users and segregate them into a particular group, cluster analysis is necessary. 

 

Without clustering, collaborative filtering will be no good since the whole concept is reliant on clustering homogenous items or commodities based on the past preferences of an individual. 

 

Perhaps collaborative filtering is a significant application of cluster analysis where the right use of this technique is performed every time and any time, giving way to a cluster-based recommender system.  

 

Conclusion 

 

To conclude, cluster analysis is based on the technique of clustering or classifying data points in a given dataset. This classification is done on the basis of similarity that implies that members of a cluster must have maximum similarity and members of 2 different clusters must have a minimum similarity. 

 

(Related blog: Clustering methods and applications)

 

On the grounds of belonging to one cluster or multiple clusters at a time, cluster analysis can be divided into hard clustering and soft clustering. All in all, this technique finds a prominent place in the world of artificial intelligence technology and other related fields.