Unsupervised Learning for Extracting Patterns and Insights

Glossary/U/Unsupervised Learning

What is Unsupervised Learning?

Unsupervised learning is a machine learning technique in which the algorithm learns patterns, structures, or relationships in data without explicit guidance or labeled examples. Unlike supervised learning, unsupervised learning does not rely on labeled data that contains predefined output labels or target variables. Instead, it focuses on finding inherent patterns and structures within the data itself.

In unsupervised learning, the algorithm aims to explore the data and discover hidden patterns or groupings without any prior knowledge of the expected outcomes. It does this by identifying similarities, differences, or dependencies among the input features or instances. The algorithm learns to extract meaningful representations or features from the data that capture important characteristics or structures.

Related terms

Machine Learning (ML)

Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that focuses on developing algor...

Data Labeling/Annotation

Data labeling, also known as data annotation, is the process of assigning meaningful tags or annotat...

Not to be confused with:

Machine Learning (ML)

Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that focuses on developing algor...

Data Labeling/Annotation

Data labeling, also known as data annotation, is the process of assigning meaningful tags or annotat...

Machine Learning (ML)

Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that focuses on developing algor...

Data Labeling/Annotation

Data labeling, also known as data annotation, is the process of assigning meaningful tags or annotat...

Machine Learning (ML)

Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that focuses on developing algor...

Data Labeling/Annotation

Data labeling, also known as data annotation, is the process of assigning meaningful tags or annotat...

Clustering is a common task in unsupervised learning, where the algorithm groups similar instances together based on their feature similarities. The goal is to find natural groupings or clusters within the data. Dimensionality reduction is another important aspect of unsupervised learning, which aims to reduce the number of input features while preserving the most relevant information. It helps in visualizing and understanding complex datasets, as well as improving the efficiency and performance of subsequent analysis or modeling tasks.

Unsupervised learning has various applications across domains, such as anomaly detection, pattern recognition, recommendation systems, and data visualization. It allows for exploratory data analysis, identifying outliers or unusual patterns, and gaining insights into the underlying structure of the data.

One of the challenges of unsupervised learning is the lack of ground truth or objective evaluation measures. Since there are no explicit target labels, it can be challenging to assess the performance or accuracy of the learned models. Evaluation often relies on qualitative assessments, domain knowledge, or comparison with known results.

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