Cluster Analysis: A Comprehensive Guide on How to Use it in Data Science

It’s hard to draw insights from large data sets without the help of a mechanism or tool. However, with cluster analysis, data scientists can assort data that share some commonality into groups. It, therefore, makes it easy to analyze and interpret massive data sets. Additionally, it helps explore and identify patterns in data sets. That is why data scientists often turn to this analysis when they have no idea how to handle huge data sets. So, if you are a data scientist interested in ways to make sense of large and complex data sets, this comprehensive guide on cluster analysis is what you need. You also get to find out how mastering cluster analysis reduces time spent on data collection, sorting, and related activities.

What is Cluster Analysis?

It is a statistical technique that groups together data points based on their features or variables. This means that data points that have similar features or variables are grouped together. The main aim of this analysis, therefore, is to identify patterns and relationships and draw meaningful insights.

Understanding Cluster Analysis with an Example

Let’s also take a look at an example to get a gist of cluster analysis in terms of how data sets are grouped together. Consider a data set of eight countries—India, the U.S., Germany, Australia, the U.K., France, China, and Canada. Using this form of analysis, we can split the countries into four clusters.

• The first cluster will consist of Canada and the U.S.
• Australia will comprise the second cluster
• The third one will consist of France, U.K., and Germany
• China and India form the fourth cluster

At first glance, we can conclude that the clusters are divided based on the continents. This is clear in the cluster composition; the first cluster consists of countries from North America. The second one comprises countries from the continental region of Australia, while the third is European nations. Finally, the fourth cluster is Asian countries. From this, it is evident that the main feature of this cluster analysis is the geographical proximity of each country.

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Characteristics of Cluster Analysis

• It helps to visualize high-dimensional data
• It further enables data scientists to deal with different types of data like discrete, categorical, and binary data
• It gives them some structure to unstructured data sets by organizing them into a group

Advantages of Cluster Analysis

• Helps to identify obscure patterns and relationships within a data set
• It helps to carry out exploratory data analysis
• It can also be used for market segmentation, customer profiling, and more

Disadvantages of Cluster Analysis

• It can be difficult to interpret the results of an ambiguous or ill-defined cluster
• The result of the analysis is affected by the choice of the clustering algorithm
• Furthermore, the success of cluster analysis depends on the data, the goal of the analysis, and the data scientist’s capability to interpret the result

Types of Cluster Analysis

• Hierarchical clustering
• Partitioning clustering
• Density-based clustering
• Grid-based clustering
• Model-based clustering

Of the ones mentioned above, the most commonly used are portioning and hierarchical clustering. Let’s look at them in detail.

Hierarchical Clustering

Hierarchical clustering investigates data clusters with a variety of scales and distances. In this approach, you create a cluster tree with a multilevel hierarchy consisting of small clusters. Then, neighboring clusters with similar features from every hierarchy are grouped together. This continues until only one cluster is left in the hierarchy. This, therefore, allows the data scientist to identify the hierarchical cluster appropriate to them.

Partitioning Clustering

Partitioning clustering considers every data point in a cluster as objects that have a specific location and distance from each other. It partitions the objects in a way that objects with the same features are close to each other, and far away from objects in other clusters.

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The Applications of Cluster Analysis

Data scientists use this analysis to collect, organize, and interpret data. Firstly, it classifies data points with similar features into a

cluster and uses it to draw conclusions. Besides that, data scientists use cluster analysis for data mining, which reduces the time taken to draw meaningful insights from data.

This analysis is also useful for marketing and sales to understand the customers’ buying behavior. You can further use cluster analysis

to form a cluster of users with similar traits and factors like income, purchasing power, and societal status. It helps in creating a personalized marketing strategy and reaching every customer segment effectively.

Streaming platforms also use this analysis to identify and group viewers having similar interests. They cluster users based on genre, minutes streamed per day, and total viewing sessions to cluster users in the group of high and low usage. This helps in placing advertisements and relevant recommendations for users.

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Cluster analysis is a powerful technique that data scientists use to draw meaningful insights from large data sets. If you are an aspiring data scientist who wants to learn about the topic in-depth, along with other data science tools and techniques, consider taking a certificate course. Emeritus offers some of the best data science courses in partnership with renowned institutes like IIM, IIT, and ISB. Join up and enhance your career prospects.

Frequently Asked Questions

1. What Happens After Getting the Results of a Cluster Analysis?

The next step after cluster analysis is data visualization. After dividing the data into clusters, data scientists represent it in a digestible manner and present it to stakeholders.

2. How do You Make Sure Your Cluster Analysis is Accurate?

Cluster tendency, number of clusters, and clustering quality are three factors that determine the accuracy of a cluster. Let’s now understand these terms further:

• Clustering tendency determines whether the clusters have a grouping structure. If they have an inherent grouping structure, your cluster analysis process is successful
• The number of clusters helps in evaluating the success of cluster analysis
• Clustering quality analyzes the similarity levels with the cluster and among other clusters

3. When to Use Cluster Analysis?

Generally, this analysis is done during the exploratory phase of research. It helps to recognize patterns, and behavior, or gain deep insights from huge data sets.