Introduction

 

As kids, we pick up a lot of things from our parents. However, we gain some information from our own experiences – we unconsciously identify patterns in our surroundings and apply them to new situations. This is how the unsupervised learning method works in artificial intelligence.

 

 

What is Unsupervised Learning?

 

Unsupervised Learning, as its name implies, uses a training dataset to create models without any supervision. Instead, using the provided data, the algorithms uncover hidden patterns and insights. It can be similar to how the human brain learns when learning something new. It is characterized as:

Machine learning techniques known as "unsupervised learning" allow models to operate autonomously after being trained on unlabeled data.

Because we have input data but no corresponding output data, unlike supervised learning, unsupervised learning cannot be used to solve a regression or classification problem. Unsupervised learning seeks to uncover a dataset's underlying structure,, group that data by similarities, and represent that dataset in a compressed format.

 

Example: Suppose an unsupervised learning algorithm is given an input dataset containing images of different types of cats and dogs. The algorithm is never trained on a given dataset, meaning it has no idea about the dataset's characteristics. The task of the unsupervised learning algorithm is to independently identify image features. An unsupervised learning algorithm performs this task by clustering the image data set into groups.

 

 

Why Use Unsupervised Learning?

 

Here are the main reasons for using "Unsupervised Learning Algorithms":-

  • Unsupervised machine learning algorithms find unknown patterns in data using pattern recognition.
  • It takes place in real-time, so all input data must be analyzed and marked in the presence of the students.
  • Algorithms/unsupervised methods help you look for features that can be useful for categorization.

 

 

Our Learners Also Read: What are Supervised Classification Algorithms?

 

 

Types of Unsupervised Learning Algorithms

 

Below are the main types of unsupervised learning algorithms.

 

Clustering

 

Every company or business must focus on understanding who customers are and what drives their purchasing decisions?

You will usually have several groups of users that can be divided according to several criteria. Age and gender are two easy criteria, whereas personal and purchasing are more complicated. Various unsupervised learning algorithm types can assist you in automating this process.

When clustering your data, it searches for any existing naturally occurring groupings. For your visitors, that can be one group of millennials with pets and another of artists in their 30s. Generally, you may change the number of clusters your machine learning algorithm searches for, allowing you to change the level of detail in those groups. You can utilize a variety of clustering techniques, including:

 

K-Means Clustering: Grouping your data points or text into "K" mutually exclusive clusters. A lot of the complexity goes into how you should choose an appropriate number for K.

 

Hierarchical Clustering: Clustering data points into parent and child clusters. You can divide your customers into younger and older age groups and then divide them into their groups.

 

Probabilistic Clustering: Grouping data points or text into clusters on a probability scale.

These variations of the same basic procedure might look something like this in code:-

Any ML clustering algorithm will usually output all your data points and the different number of clusters they belong to. It's entirely up to you to decide what they mean and what precisely the ML algorithm found. As with most data science – unsupervised learning can only do so much: value is created when people connect with outputs and create meaning.

 

Data Compression

 

Despite significant advances in computing power and storage costs over the past few years, it still makes sense to keep your data files as small and reliable as possible. This clearly means that you only run the ML algorithms on the necessary data and don't train too much. Unsupervised learning algorithms can help with this through a procedure known as dimensionality reduction.

Dimensionality reduction (dimensions refers to the number of columns in your dataset) relies on many of the same concepts as information theory: it assumes that a lot of data is redundant and that you can only represent a small fraction of the actual content of most data in a dataset.

Generally, it means combining parts of your knowledge uniquely to convey meaning. Several well-known ML algorithms are commonly used to reduce dimensionality:-

 

  • Principal Component Analysis (PCA): Finds linear combinations that communicate most of your dataset variances.
  • Singular-value decomposition (SVD): Factors your details into the product of three other smaller matrices.

 

These techniques and some more complex relatives rely on linear algebra concepts to break down a matrix into more digestible and informative parts.

Reducing the dimensionality of your information can be a vital part of a promising ML pipeline. Take this example of an image central to the emerging discipline of computer vision.

If you could reduce the size of your training set by order of magnitude, it would significantly reduce your computing and storage costs and make your ML models run much faster. This is why PCA is often run on images during pre-processing in advanced ML pipelines.

 

Generative Models

 

In generative models, new samples are generated from the same distribution as training data using an unsupervised learning method. To produce similar data, these ML models must successfully identify and learn the essence of a particular data set. The long-term advantage of this type of model is its ability to automatically learn the properties of the provided data.

A basic example of generative models is a set of image data or text. Given a set of base images, a generative model could generate a set of images similar to the given location.

 

 

Real-Life Applications of Unsupervised Learning

 

 

Market Basket Analysis

 

It is a machine learning model based on an algorithm that predicts if purchasing one group of goods will make you less or more inclined to purchase another.

 

Semantic Clustering

 

Words with similar semantics are used in similar contexts. People use their own methods to submit their questions to the website. In order to help customers quickly and easily discover the information they need, semantic clustering puts all of these replies with the same meaning into a cluster.

 

Delivery Store Optimization

 

Machine learning models are used to predict demand and keep up with supply. They are also used to open stores where demand is higher and to optimize roots for more efficient delivery based on past data and behavior.

 

Identification of Accident-Prone Areas

 

Unsupervised Machine Learning models can be used to find regions that are prone to accidents and put safety measures in place based on how severe those accidents are.

 

 

Conclusion

 

Unsupervised learning algorithms run without the help of a supervisor. The input data fed to ML algorithms are unlabeled, i.e., no output is known for each input. The algorithm detects trends and patterns in the input data and makes connections between different attributes of the input.

Unsupervised learning helps find pattern recognition in data, clustering data, and real-time analysis.