Unsupervised Machine Learning

What Is Unsupervised Machine Learning?

Unsupervised machine learning is a type of machine learning (ML) whereby a software system utilizes datasets to make inferences – but it does so without the need for the manual operation of data labeling. It is sometimes shortened to UML.

In other words, unsupervised machine learning is a self-training system whose development does not involve the human-led task of annotating things, such as images and audio files, to help train the ML software to recognize its surroundings.

How Does Unsupervised Machine Learning Work?

Unsupervised machine learning works by using data mining techniques and assigning categories to the gathered data points. It is by forming such categories that a UML system, regardless of its application, is able to make inferences about the data points – whether they’re formed of images, audio transcripts, numerical data, or otherwise.

While there are many names for the various techniques whereby UML algorithms can categorize data points, the chief one is data clustering.

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What Is Data Clustering in UML?

This is a type of data classification task in which the UML system takes unlabeled data points and groups them based on similarities in their features, then looks for patterns that emerge. These databases are usually sourced from such places as data centers and data warehouses. Though this process uses unlabeled data, and can be a useful preprocessing routine for the labeling task, it is not a replacement for manual data labeling.

Accordingly, while an unsupervised machine learning system doesn’t intuitively recognize things in the same way that humans do, it is able to infer what something is based on the criteria that it can be assigned.

To provide a very basic example of the UML process of data clustering, consider a situation where you feed an unsupervised machine learning system one hundred pictures of cars.

While the software wouldn’t be able to initially specify what a car is, it’s possible that its algorithms would recognize patterns and conclude that – unless it’s faced with nuances or discrepancies, such as a three-wheeled car – any and all pictures of a four-wheeled box with seats should be grouped under the term car.

Why Is Unsupervised Machine Learning Important?

Unsupervised machine learning is important because it is a form of automation that is crucial to the development and accomplishment of processes that are time-consuming, labor-intensive – and possibly even mind-numbing – for humans to carry out.

It is also important because it is versatile: the number of UML applications is countless and growing, and it’s already making strides in image recognition, industrial robotics, and fraud prevention.

A good example of the importance of unsupervised machine learning is in the subset of robotics known as picking and placing. While a human, such as a warehouse worker, is well-equipped to find, pick up, and place the right inventory in its assigned areas, UML systems allow logistics robots to do the same three tasks with little or no human supervision.

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How Can Unsupervised Machine Learning Fight Fraud?

Unsupervised machine learning helps fight fraud by clustering data and detecting anomalies, such as suspicious transactions, within its data clusters. Specifically, UML categorizes online activity and flags any grouped data points that appear unusual or are deemed suspicious based on set parameters.

In the context of using unsupervised machine learning to fight fraud, the process of data clustering, i.e. categorizing data points, proceeds the process of anomaly detection.

Anomaly detection is a core example of how useful USM is to fighting fraud: Similarly to graph neural networks, it is able to find suspicious patterns that emerge in otherwise consistent datasets.

For example, a USM system could cluster a list of online transactions, and in that process, determine that there are anomalies in one user’s interaction with an ecommerce site compared to the others. Perhaps they purchase many expensive products all in a single day: this is a calling card of banking identity theft, and unsupervised machine learning could expedite the process of finding such suspicious behavior.

While a fully automated process may lead to false positives, using such a machine learning tool to augment manual fraud investigations is an increasingly airtight security equation. When working in tandem, the results that a well-educated fraud team can achieve are a testament to the vital data processing capabilities of unsupervised machine learning as it is used to detect, flag, and ultimately fight fraud.

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