Classification vs Clustering: What You Should Know

Artificial intelligence (AI) and machine learning are growing quickly across global industries. According to the International Data Corporation (IDC), the Indian AI market is expected to grow at a compound annual growth rate of 20.2 percent and reach $7.8 billion by 2025 [1]. With so many companies adopting AI and machine learning technologies to improve their operations, professionals with skills in these areas are growing in demand. 

 Machine learning uses several methods to identify patterns within data and identify common characteristics between groups. Classification and clustering are two data mining techniques you can use to identify patterns and examine data, and distinct differences set them apart. Understanding the core principles of machine learning can help you build the foundational knowledge needed to excel in this field and apply new techniques within your industry. 

This article will discuss the difference between classification and clustering, the classification and clustering methods, and real-world examples showing how mastering these techniques may benefit you.

What is data mining?

Data mining is taking a large data set and identifying trends, patterns, and explanatory information needed to understand its implications. It is typically done through mathematical analysis that methodically sifts through the data and sorts it based on patterns that help contextualise it. Because of the high volume of information, using machine learning and AI algorithms is essential for professionals to categorise the information effectively.

Standard data mining techniques include classification, clustering, association analysis, data characterisation, data discrimination, outlier analysis, and evolution analysis. While each method has advantages, clustering and classification are two that data professionals commonly choose.

Key differences between classification and clustering

One key difference between classification and clustering techniques is the learning structure. Supervised and unsupervised learning are the two basic approaches to machine learning. Supervised learning uses labelled data sets to train the algorithm to work a certain way, involving labelled inputs and outputs. Classification is an example of supervised learning. Unsupervised learning uses unlabelled data sets, and the algorithm looks for hidden patterns humans do not structure. Clustering is an example of unsupervised learning.

Classification inputs data into class labels based on characteristics while clustering groups data points based on similarities recognised by the software. Classification is a more complex technique than clustering, as classification algorithms can have many levels of classification structure. Classification uses techniques such as logistic regression, support vector machines, and Naive Bayes classifier, while clustering utilises different techniques. 

What is clustering?

Clustering is a statistical analysis technique that classifies each data point into a relevant cluster. Each cluster has specific characteristics that link each data point within it. The idea is that sorting data points into clusters reduces the data set and helps you more clearly understand trends. Clustering is commonly used in machine learning and data science and is considered an unsupervised machine learning method.

Five key clustering methods you can use in machine learning are:

• Partitioning clustering

• Hierarchical clustering

• Fuzzy clustering

• Density-based spatial clustering of applications with noise (DBSCAN)

• Distribution model-based clustering

Partitioning clustering

Partitioning clustering is separating the data into a specified number of clusters. You will generally decide on a certain number of clusters, and then the machine learning algorithm will divide data into the appropriate groups. These groups are called "k partitions". The algorithm then estimates the centre of each partition and coordinates the data.

Hierarchical clustering

With hierarchical clustering, the clusters form through an iterative process. You can visualise this as a tree. There’s an initial branching where the data originally divides, and then each "branch" further divides into smaller branches. Depending on your needs, this top-down approach allows you to work with more broadly or narrowly defined clusters.

Fuzzy clustering

Fuzzy clustering allows you to include data points or associate them with several clusters. In this method, you characterise each data point by the probability of it being in several clusters. Fuzzy c-means is a widely used technique for characterising data.

Density-based spatial clustering of applications with noise (DBSCAN)

This method works in similar ways to the human brain. It is the fastest clustering method, but there must be a clear search distance, and clusters must have similar densities. For this method, you identify clusters by regions of high densities of observations and separate them from areas with low density. 

Distribution model-based clustering

Based on Gaussian distribution principles, you perform this type of clustering by dividing data based on their probability of belonging to different probability distributions. 

What is classification?

Classification is a technique used in machine learning to categorise elements within a data set. Classification algorithms use labelled data sets to assess how data fits within specific, predetermined categories. These are the four main types of classification:

• Binary classification

• Multi-class classification

• Multi-label classification

• Imbalanced classification

Binary classification

Binary classification categorises data into two distinct categories. You generally use binary classification when you have two clear groupings and no middle ground. For example, you may label emails as "important" or "unimportant". A patient may be labelled as "completed an appointment" or "did not complete an appointment" for medical records. Logistic regression, decision trees, and Naive Bayes are common algorithms used for this type of classification.

Multi-class classification

This type of classification categorises data into several known categories. For example, you may use this type of algorithm for picture recognition. You might analyse an image of a tree and classify it as likely belonging to a particular group of trees, such as an oak or palm tree. Decision trees, k-nearest neighbours, and random forests are popular algorithms for this purpose.

Multi-label classification

Multi-label classification can predict several class labels for each data point instead of a singular classification label output, as in binary and multi-class classifications. For example, you may scan an image and classify it into several groups depending on its content. For example, you might classify a fruit basket into "apple," "orange," and "pineapple" groups. Multi-label decision trees, multi-label random forests, and multi-label gradient boosting are common with this method.

Imbalanced classification

Imbalance classification is suitable for unequally distributed classification tasks. This typically occurs when the outcome is binary, but there will be more data in one category than the other. Fraud detection, medical diagnostics, and outlier detection commonly use this technique. 

Real-world examples

Classification and clustering are commonly used across several industries. By boosting your knowledge and expertise of these concepts, you may expand your ability to apply machine learning knowledge across sectors and open career opportunities. For example, you may use classification to determine user intent.

Take shoppers, for instance. Companies that sell products may want to know whether a shopper is more likely to window shop or shop online. Using clustering techniques, companies can segment their customers into specific user groups.

These groups can present common characteristics, such as age, gender, type of family, and more. If a company were to target online shoppers and wanted to develop a campaign, it could look at the characteristics of the cluster to best target its online customer base. 

Financial firms commonly use classification in fraud detection. Because online transactions are increasingly common, detecting fraud accurately is crucial to protect customers' financial information. To better identify fraud, financial institutions are using classification algorithms to take historical transaction data and identify patterns that may indicate suspicious activity. 

Next steps

Several course offerings on Coursera allow you to increase your machine-learning skills and expand your job opportunities in this industry. Consider a Professional Certificate such as IBM Data Science by IBM Skills Network, or complete a Specialisation such as Deep Learning by DeelLearning.AI.