What is Unsupervised Learning?

What is Unsupervised Learning?

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Updated on Sep 8, 2022 22:48 IST

Machine Learning is categorized in majorly 4 types and unsupervised algorithm is one of them. In order to understand what is unsupervised learning and how it differs from supervised learning let’s proceed further.


Unsupervised learning is the type of machine learning algorithm that learns and finds clusters and patterns from the unlabeled datasets. These algorithms helps in uncovering hidden patterns without the need for human interaction. The ability of unsupervised learning algorithm makes it most suitable for many use cases like credit card fraud detection, customer segmentation, movie recommendation, in cybersecurity where the attackers are always changing methods, extracting most important features for training machine learning algorithm for best results and many more.

If you are new to machine learning here’s a blog to understand what is machine learning in simple terms.

In this blog on what is unsupervised learning that we will be covering the following topics:

Table of Content

  1. Why use Unsupervised Learning? 
  2. Understanding Unsupervised Learning Technique
  3. Types of Unsupervised Learning Algorithm
  4. Disadvantages of Unsupervised Learning
  5. Applications of Unsupervised Learning
  6. Difference between Supervised and Unsupervised Learning 
  7. Conclusion

Why use Unsupervised Learning?

There are numerous advantages to using unsupervised machine learning algorithms on your data. The following are some of the most prevalent reasons why people choose unsupervised learning in the industry.

  1. Data labelling requires a large amount of manual work and cost. Unsupervised learning deals with the issue by learning to categorize data without labels.
  2. It is much easier to add labels once the data has been categorized.
  3. It is best used when you want to find patterns but don’t know exactly what you’re looking for in the data.
  4. Helps in important features selection in your dataset to improve the accuracy score of the trained model.
  5. Helps in reducing the number of features using dimensionality reduction techniques like LDA, PCA, etc.
  6. It is extremely useful for discovering patterns in data that would be impossible to discover using traditional approaches.
  7. Perfect tool for data scientists dealing with finding patterns in unstructured and unlabeled raw data.
  8. Probabilistic approaches can help in determining how comparable the data are.

Understanding Unsupervised Learning Technique

Unsupervised learning is a form of machine learning algorithm that finds clusters in the un-labelled datasets. Unsupervised machine learning methods are mostly used to classify unlabeled data based on similarities and patterns found in the dataset. The word “unsupervised” means, unlike a supervised learning system, the algorithm is not guided with labeled data. 

Learn more: Difference between Supervised and Unsupervised Learning


Raw input data is fed to the machine without any label for interpretation. The unsupervised model is trained on the data with a unsupervised learning algorithm. More data will be required for better results. Once the model is trained the model will be able to cluster the dataset based on similar patterns.

Consider image data from apples, banana, and mangoes together. The objective is to train the model to differentiate between the image of the fruits. The algorithm will identify objects based on their form, size, and color.

Apple is a tiny, spherical fruit that is red in color. Banana, on the other hand, is longer, elongated in shape, and yellow in color. Based on these criteria, the model will learn and discriminate. The data points that resemble an apple will form a single cluster. Similarly banana and mango will be sorted in a different cluster.

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Types of Unsupervised Learning

Unsupervised Learning usages are majorly categorized as two types:


Any company or business must focus on knowing its customers: who they are and what drives their purchasing decisions.

Typically, you’ll have numerous groups of users that may be separated based on a few characteristics. These factors might be as straightforward as age and gender or as sophisticated as a persona and buying process. Unsupervised learning algorithms of many types can assist you in automating this job.

Clustering will search your data for natural clusters if they exist. For your visitors, this may imply one group of artists and another of millennials with pets. You can normally change the number of clusters that your ML algorithm looks for, which allows you to customize the granularity of these groupings.

Data Compression using Dimensionality Reduction

Even with recent advances in processing power and storage prices, it still makes sense to keep your data sets as minimal and dependable as feasible. That certainly implies running ML algorithms on only the essential data and not training on too much. Dimensionality reduction is a method that Unsupervised Learning Algorithms may help with.

Dimensionality reduction (the number of columns in your dataset) is based on many of the same notions as Information Theory: it implies that a lot of data is redundant and that you can represent the majority of the data in a data set with only a tiny fraction of the actual content.

In general, this involves mixing different aspects of your knowledge in unique approaches to convey meaning.

Types of Unsupervised Learning Algorithm

Some of the most widely used unsupervised learning algorithms for dealing with unlabeled datasets are:

  1. K-Means Clustering
  2. Hierarchical Clustering
  3. Fuzzy C-Means Clustering
  4. Principal Component Analysis (PCA)
  5. Linear Discriminant Analysis (LDA)
  6. Neural Network
  7. Apriori Algorithm
  8. Hidden Markov Model

Learn more: Top 10 important machine learning algorithms asked in Data Science Interview

Learn more: 50+ Most important machine learning interview question by Naukri for this year

Disadvantages of Unsupervised Algorithm  

Unsupervised learning has many advantages, ranging from the ability to identify relevant insights in data to the elimination of time-consuming data labeling operations. However, there are several hazards to be mindful of when using this method to train machine learning models. Here are a few points 

  1. As algorithms must study and calculate all possible outcomes, the training process takes a long time 
  2. The input data does not contain labels as response keys, and the answers generated by unsupervised learning models may be less reliable 
  3. Unsupervised learning frequently deals with large datasets, which can increase the computational complexity 
  4. The method necessitates output confirmation by people, either internal or external specialists familiar with the subject field 

Applications of Unsupervised Learning

  1. Customer Segmentation by creating pattern based clusters for target marketing
  2. Face recognition feature in your mobile phones are majorly trained on unsupervised learning algorithm.
  3. Movie/ Product Recommendation based collaborative filtering, content-based filtering or its hybrid.
  4. Anomaly Detection in cases Credit card fraud detection with unlabeled and unbalanced dataset
  5. Creating labeled data (based on clusters) for training a supervised learning model

Check out: Popular Unsupervised Learning Course from Coursera

Challenges in implementing Unsupervised Algorithms

Because there are no labels in Unsupervised Learning Algorithms, determining the accuracy of your ML system is practically difficult. In clustering, for example, how can you tell if K-Means found the correct clusters? Are you using the right number of clusters? We can use a precise score; however, you should be a little more innovative here.

The answer to the question “will Unsupervised Learning Algorithm work for me?” is heavily dependent on your business situation. Clustering will only function properly in our visitor/customer segmentation example if your consumers fit into natural categories.

Implementing your unsupervised learning model in the real world and observing what occurs is one of the best (but riskiest) ways to test it! Designing an A/B test with and without the clusters your algorithm output can be an efficient method for assessing whether the information is relevant or incorrect.


Unsupervised Learning Algorithms function without the help of a supervisor. The input data fed into the ML algorithms is unlabeled data, which means that no output is known for each input. The algorithm detects trends and patterns in the input data and establishes a link between the input’s various qualities.

Algorithms in the supervised learning model are taught using labelled data, whereas Algorithms in the unsupervised learning model are learned using unlabeled data. Unsupervised learning is excellent for discovering patterns in data, establishing data clusters, and doing real-time analysis.

Unsupervised Learning Algorithms include tasks such as clustering and data compression using dimensionality reduction. The main disadvantage of unsupervised learning is that it does not provide exact data sorting information.


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What is unsupervised learning example?

Unsupervised learning example includes k means algorithm, c means algorithm, hierarchal clustering, dimensionality reduction and many more.

What is difference between supervised and unsupervised learning?

The most obvious difference supervised and unsupervised learning is the label. Unsupervised learning algorithm uses unlabeled dataset, whereas the supervised learning algorithm uses a label dataset.

Why unsupervised learning is important?

Unsupervised learning algorithm helps in finding pattern in unlabeled dataset especially when you don't know what to look for in the data. It can also be used to label the dataset for supervised learning model's training.

Where is unsupervised learning used?

The ability of unsupervised learning algorithm makes it most suitable for many use cases like credit card fraud detection, customer segmentation, movie recommendation, in cybersecurity where the attackers are always changing methods, extracting most important features for training machine learning algorithm for best results and many more.

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