1.
Introduction
2.
Classification
2.1.
Types of Classification Techniques
3.
Clustering
3.1.
Types of Clustering Techniques
4.
Difference Between Classification and Clustering
5.
5.1.
What is Classification?
5.2.
What is Clustering?
5.3.
When do we use clustering?
5.4.
When do we use classification?
5.5.
Can clustering and classification be used together?
6.
Conclusion
Last Updated: Mar 27, 2024

# Difference Between Classification and Clustering

## Introduction

Hello Ninjas! In this article, we will discuss the difference between classification and clustering. Four machine learning techniques exist supervised, unsupervised, semi-supervised and reinforcement learning. Classification is a supervised learning technique, whereas clustering is an unsupervised learning technique.

Letâ€™s begin by first learning what classification is.

## Classification

It is one of the most popular machine-learning techniques. It classifies input data into a class, labels, or categories. It is employed in various applications such as facial recognition, image classification, spam filtering, fraud detection, voice recognition, etc. It is a supervised learning method in which the model uses a labelled dataset to train their weights to predict the category or class of unseen data points. For further information on classification, check out this article.

### Types of Classification Techniques

Based on the number of classes or categories to classify, there are two types of classification techniques:

1. Binary Classification: When the data is classified into only two categories, the problem comes under binary classificationâ€”for example, breast cancer classification, spam email filtering, fake news detection, etc.

2. Multi-Class Classification: When the data is classified into more than two categories, the problem comes under multi-class classificationâ€”for example, image classification, voice recognition, face recognition, etc.

Some basic classification algorithms include Logistic Regression, Decision Trees, Naive Bayes, Random Forest, Support Vector Machine (SVM), etc.

The above algorithms can be further classified into linear and non-linear classifications based on whether the data points are linearly separable. For further information, visit this blog.

## Clustering

It is used to group or divide a dataset into multiple groups or clusters of similar data points. This division is based on data characteristics or features. As it is an unsupervised learning technique, it does not require a labelled dataset for clustering the data points. It involves analysing or identifying patterns or similarities between the data points, and similar data are grouped. The similarities are based on various features of the data. For example, One can use the number of wheels of a vehicle to identify whether a vehicle is a car or a bike. So, the number of wheels is a feature that can be used for clustering cars and bikes.

### Types of Clustering Techniques

There are four types of clustering techniques:

1. Hierarchical Clustering: This technique creates a hierarchy of clusters by merging either smaller groups into larger ones or dividing larger groups into smaller ones. For example, files and folders are clustered on hard disks, etc.

2. Density-based Clustering: This technique identifies higher data point densities and considers their clusters. Points in lower density are considered to be noise or outliers.

3. Centroid-based Clustering: This technique divides the dataset into clusters based on the number of centroids. Each data point lies in one of the centroidâ€™s regions.

4. Distribution-based Clustering: If the data points follow a specific distribution like the Gaussian distribution, then this method divides the data into different clusters based on the probability of the data belonging to the group.

Some basic clustering algorithms include K-means, DBSCAN, Spectral Clustering, etc.

## Difference Between Classification and Clustering

Here are some critical differences between classification and clustering:

### What is Classification?

Classification assigns labels or categories to objects based on their attributes or features.

### What is Clustering?

Clustering is grouping similar objects based on their characteristics or attributes.

### When do we use clustering?

Clustering is frequently used to find patterns or groupings in the data. Additionally, it can be applied to projects like consumer segmentation or anomaly detection.

### When do we use classification?

Classification is often used for tasks such as image recognition, text classification, or predicting customer churn.

### Can clustering and classification be used together?

Yes, they can. For example, clustering can identify groups of objects with similar characteristics. Then one train classification model to predict the category or label of new things within each group. The above process is known as semi-supervised learning.

## Conclusion

In this article, we have learned the basics of classification and clustering and the difference between classification and clustering. We also saw different algorithms for each kind and their use cases. To learn more about these techniques, you can check out our other blogs:

You may refer to our Guided Path on Code Studios to enhance your skill set on DSACompetitive ProgrammingSystem Design, etc. Check out essential interview questions, practice our available mock tests, look at the interview bundle for interview preparations, and so much more!

Happy Learning, Ninjas!

Live masterclass