Ever imagined how a machine learns on its own? Well, it is through various machine learning techniques, specifically Unsupervised Machine Learning.

Unsupervised Machine Learning enables AI applications to learn and find patterns in large data sets without human involvement. This type of Machine Learning is necessary for achieving general Artificial Intelligence.

 Unsupervised Learning is popular when there is huge data and labeling is time-consuming and labor-intensive. It is also an impractical process. Unsupervised Learning creates a huge difference by letting AI applications learn without any supervision.


Also read:What is Supervised, Unsupervised Learning, and reinforcement learning in Machine Learning


What is Unsupervised Machine Learning?

As discussed above, it is a unique ML technique for identifying patterns in unlabelled data sets. Here, the AI system is provided with input data only. Without the involvement of human supervision, the process of Unsupervised Learning takes place. The machine learns on its own by observing data and finding patterns. Without any external guidance, the Machine Learning system acts accordingly.

This technique is popularly used for creating AI systems without human intelligence. They are enabled to take independent decisions by analyzing huge volumes of unlabelled data.

The algorithms used in Unsupervised Learning are more efficient at performing complex processes. The accuracy of results may vary. Artificial neural networks that help in deep learning are the framework for Unsupervised Learning. Unsupervised Learning is extensively used due to its uniqueness and necessity. The most common needs are mentioned below.

  • Huge amount of unlabelled data
  • Labeling data is tough and requires human labor
  • Unsupervised Learning makes this process easier
  • Effective in analyzing unknown raw data
  • Very useful for pattern recognition in huge data sets
  • Mainly of two types namely parametric Unsupervised Learning and nonparametric Unsupervised Learning

Type of Unsupervised Machine Learning

Now let us discuss the different categories of Machine Learning in the Unsupervised Learning techniques. Mainly the UL problems can be divided into two types – clustering and association problems.

Clustering or cluster analysis is the process of making clusters by putting objects in different groups. The objects possessing similarities are grouped whereas the others are grouped in different clusters. Clustering can be divided into four categories such as – exclusive clustering, hierarchical clustering, overlapping clustering, and probabilistic clustering.

Association or association rule learning is a method in Unsupervised Learning to find relations between variables in huge data sets. This method is used extensively for handling non-numeric data points. In simple words, it is a technique to find associations between variables. For example, people who buy groceries are most likely to buy dairy products. This type of learning is effective in the retail industry.


Also read: Top 50 interview questions of Machine Learning


Unsupervised Machine Learning Algorithms

The algorithms which help in both clustering and association rule learning are mentioned below. The algorithms which help in both clustering and association rule learning are mentioned below.


Clustering Algorithms 

K-means clustering: Extensively used in the field of data science. k-means clustering algorithm groups the similar objects together, k represents the number of clusters. The number of clusters 5 means k value is 5 and vice versa. It divides the unlabeled dataset into groups with similar properties. Cluster centroids or K are calculated, each cluster has one centroid.

Principal component analysis (PCA): It is used for reducing the dimensionality of large datasets. Large numbers of variables are converted to smaller datasets for easing the process of analysis. The accuracy might get affected by reducing the number of variables. But here, simplicity is preferred over complete accuracy. 


Association Rule learning Algorithms

Apriori algorithm: This is used for data mining. It is primarily used for mining databases with a large number of transactions such as a list of items bought in a retail shop, harmful effects of certain drugs, items usually bought together by customers, and many more. It uses the horizontal data format and scans the database multiple times to identify frequent items.


ECLAT algorithm: Equivalence Class Clustering and bottom-up Lattice Traversal is a data mining algorithm used for the itemset mining and finding frequent items. ECLAT has a vertical data format and scans only once thus, it is usually faster.


Frequent pattern (FP) growth algorithm: It is a better version of the Apriori algorithm. The data is represented in the form of a tree also known as a frequent tree or pattern. 


Also read: Model vs Algorithm in ML

How Unsupervised Machine Learning Works?

Let us now try to understand how algorithms are utilized in Unsupervised Machine Learning for analyzing huge data sets.

In simple words, UL is based on analyzing uncategorized or unlabelled data and finding patterns in it. The input for Unsupervised Machine Learning is massive volumes of data without any labels or categories. The process of UL begins training algorithms using the acquired unlabelled datasets. The algorithms recognize patterns and categorize the data points based on identified patterns. 

Now imagine yourself tasting a dish made with white sauce and another dish made of red sauce. The first clear distinction between the two dishes comes from their different visible red and white color. The second difference is noticed as soon as it hits the taste buds. UL Machines also learn similarly. They identify the distinguishable features and categorize the data points accordingly. However, in Supervised ML, the dishes would be labeled with the sauce used in preparing them.

Examples of Unsupervised Machine Learning

Some of the common applications of Unsupervised Machine Learning in the real world are described below.


Detection of the anomaly: an effective way to identify fraud activities by finding typical data points in data sets.


Computer vision: used extensively for image recognition and object identification such as in self-driving cars and the healthcare industry for disease diagnosis.


Recommendation systems: it is based on analysis of customers buying historical data and recommending products to customers.


Customer personality: based on purchase habits and analysis of such data, you and learning help in building better businesses by identifying accurate customer needs.

Challenges of Unsupervised Machine Learning

Unsupervised Learning has countless benefits but at the same time, it poses many challenges which require human interference. The primary challenges faced by data scientists include the following.


  • The high volume of training data poses complexity issues for computational processes
  • The training duration or time is usually longer
  • The risks for inaccurate results are also high
  • The validation of output results require human intelligence
  • Data clustering takes place entirely by the wheel of the machine and hence lacks transparency

Applications of Unsupervised Machine Learning

The primary motive of utilizing Unsupervised Machine Learning for data science is to identify the hidden patterns and popular trends in the given data set. Unsupervised Machine Learning is used averagely due to its limitations. Unlike Supervised Learning, they do not create personalized results rather group the outcomes on broad parameters. The main idea of using Unsupervised Machine Learning is when the outcomes are not specifically desired. However, it is effective for quick categorization of the customer needs or any other similar categorization for huge raw data. 


As discussed above, UL is used for anomaly detection, computer vision, fraud detection, discovering broken hardware pieces, identifying outliers, and similar other applications. It is also used for Association mining where similar sets of items are grouped. The basket analysis is based on this technique. It offers the customers the choice to buy items together by recommending frequently bought together items. It is impossible to perform association mining without clustering items and clusters are formed when clustering algorithms are applied in Unsupervised Learning.


Another popular application of Unsupervised Learning is dimensionality reduction. This refers to the reduction in the number of features in a dataset for better reprocessing. Unsupervised Learning algorithms are used in latent variable models. The results of Unsupervised Learning can be later on applied for Supervised Learning algorithms.


Also read: Regression Techniques in Machine Learning

Unsupervised Machine Learning vs Supervised Machine Learning

The clear-cut distinction between Supervised Learning and Unsupervised Learning is that Supervised Learning is heavily dependent on someone to supervise the entire process of data training. In Supervised Learning, the outcomes are highly accurate since the solutions are already trained to the model. On the other hand, Unsupervised Learning does not require any outside interference. It just produces results based on the inputs. There is no right or wrong in this process however, the accuracy is a point of concern.


From the perspective of mathematics, programming, and computation, Unsupervised Learning is way more complex, time-consuming, and complicated as compared to Supervised Learning. However, it is highly useful in data mining to gain insights into data structure before assigning any classifier or a Machine Learning algorithm for automatic data classification.


And label raw data is enormous and Unsupervised Learning can cause trouble. The Supervised Learning data sets are already validated by human intelligence to ensure model accuracy. But in Unsupervised Learning models, the validation is not ensured. Despite that, Unsupervised Learning is performed first and then Supervised Learning to identify features and create classes. It is an online process whereas Supervised Learning takes place in offline mode. This means that you and algorithms process data in real-time. Supervised Learning is classified into regression and classification whereas Unsupervised Learning algorithms are association and clustering-based problems.

Other important learning methods include semi-Supervised Learning and reinforcement learning.


Artificial Intelligence and Machine Learning are highly complex fields of study. They are the future of the world, yet so much remains unexplained due to high complications. The algorithms and programming are key to producing better ML models and their utilization. The primary goal for a beginner working on UL and training is to understand the key concepts and common algorithms used. Without a thorough knowledge of UL, it’s hard to train raw unlabelled data. 


We hope this blog enlightened you on understanding the aspects of Unsupervised Learning and its applications. To read similar content and blogs, visit our website now.