Unsupervised Machine Learning

We will discuss Unsupervised Learning. Unsupervised means to act without anyone’s supervision or without anybody’s direction. Now, here the data is not labeled. There is no guide and the machine have to figure out the data set given and it has to find hidden patterns in order to make predictions about the output. Example of unsupervised learning is an adult-like you and me. We do not need a guide to help us with our daily activities. We can figure things out on our own without any supervision.

Definition

Unsupervised Learning is a type of Machine Learning algorithms. The data provided to the machine is not labeled and the machine has to learn without any supervision. So that’s why it should discover hidden patterns and trends in the data.

Types of Problems

This type of learning can be used to solve association problems and clustering problems. Association problems basically involve discovering patterns in data finding co-occurrences and so on. A classic example of Association rule mining is a relationship between bread and jam. So, people who tend to buy bread also tend to buy jam. It’s all about finding associations between items that frequently co-occur or items are similar to each other.

unsupervised learning example

Apart from Association problems unsupervised learning also deals with clustering and anomaly detection problems. Clustering is used for cases that involve targeted marketing. Wherein you are given a list of customers and some information about them and what you have to do, is you have to cluster these customers based on their similarity. Digital AdWords use a clustering technique to cluster potential buyers into different categories based on their interests and their intent.

Anomaly detection, on the other hand, is used for tracking unusual activities. An example of this is credit card fraud wherein various unsupervised algorithms are used to detect suspicious activities.

Type of Data

unsupervised learning example desireAI

In Unsupervised learning, the machine is only given the input data. So here we don’t tell the system where to go. The system has to understand itself from the input data that we give to it. So, it does this by finding patterns in the data? So, if we try to classify images into cats and dogs. In Unsupervised Learning, the machine will be fed images of cats and dogs and at the end, it will form two groups, one containing cat and the other containing dogs. Now the only difference here is that it won’t add labels to the output. It will just understand how cats look and cluster them into one group and similarly for dogs.

Training

In unsupervised learning, the training phase is big because the machine is only given the input and it has to figure out the output on its own. So, there’s no supervisor here or there’s no mentor over here.

Aim

Unsupervised learning is all about discovering patterns and extracting useful insights. Now since the algorithm is only fed the input. It has to find a way to get to the output by finding trends and associations in the data.

Approach

In unsupervised learning, the algorithm has to find patterns in data. Trends in data and keep exploring the data until it reaches the output.

Output Feedback

In Unsupervised learning, there is no feedback mechanism because the machine is unaware of the output during the training phase.

Popular Algorithms

We have algorithms like K-Means and C-Means for clustering analysis and algorithms like Apriori and Association rule mining to deal with association problems.

Applications

The recommendations you see when you shop online like for example, if you buy a book on Amazon you get a list of recommendations. Now, these are all done by unsupervised learning algorithms other applications include Anomaly detection, credit card fraud detection and so on. Here is some more popular application of machine learning.

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