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What is Unsupervised Machine Learning?

June 21, 2023

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Machine learning can use two types of learning approaches: supervised and unsupervised. In supervised learning, the computer is trained on labeled data while unsupervised learning helps identify hidden structures and patterns in data. Though unsupervised learning is not as popular as supervised, it has a powerful potential for analysis.

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Understanding Unsupervised Learning

Unsupervised learning in AI operates on unlabeled data, meaning data without predefined target labels. Unlike supervised learning, where algorithms learn from labeled examples, unsupervised learning algorithms aim to get meaningful insights from raw, unstructured data. The process involves identifying patterns and relationships within the data without external guidance.

Techniques Used


Clustering is a primary tool for unsupervised learning. With clustering, algorithms find natural groupings or “clusters” within a dataset by comparing data points’ similarities and differences. 

Dimensionality Reduction

Datasets can be hard to examine and visualize because they contain many features and variables. Unsupervised learning helps with this by using techniques like Principal Component Analysis (PCA) or t-SNE to reduce complexity. These techniques transform high-dimensional data into a simpler form while still holding essential information.

Anomaly Detection

Unsupervised learning also plays an important role in detecting unusual behavior or anomalies. By learning patterns from normal data, unsupervised algorithms can detect and flag unusual or anomalous instances. Applications for this include fraud detection, network security, and manufacturing quality control.

Unlock the Full Potential of Your Data

Embracing unsupervised learning allows us to extract knowledge from vast amounts of unstructured data, transforming it into actionable information. It is an indispensable tool for data scientists and analysts seeking to make sense of unlabeled data.

The Virtue Insight IaaS platform provides users a robust infrastructure and tools for building and deploying AI models. Within this platform, unsupervised learning algorithms can be leveraged to uncover valuable insights from vast amounts of data.

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