Supportsoft Glossary

Unsupervised Learning for Hidden Pattern Discovery

The unsupervised approach to learning in Machine Learning (ML) is one where the training data is not pre-labelled with answers; instead, it allows the model to explore the data without any preconceived notions of how to group similar observations or find general trends or relationships among observations. 

Companies leverage unsupervised learning techniques to segment customers into different categories, identify anomalies, perform market research, and find additional relevant information from data collected that was not found in traditional analysis. Unsupervised ML provides companies with a new perspective on the way they view their markets, trends, etc.

There are many ways companies in the Information Technology and Information Technology Enabled Services (ITES) space use unsupervised ML, including helping to assess system health (also known as performance monitoring) by providing network analysis, identifying performance issues early and allowing organisations to optimise their networks.

The most commonly used techniques are clustering and dimensionality reduction. Clustering allows for clustering observation (similar data points) into separate groups, and dimensionality reduction helps to simplify complex datasets while revealing underlying structures that support data-driven business decisions, promote innovation and drive revenue growth.

By allowing organisations to work with unstructured data and discover hidden patterns, unsupervised ML helps them better understand their data and identify new areas for improvement.