Unsupervised Learning

Unsupervised Learning

Unsupervised Learning is a type of machine learning where the model learns patterns and structures from unlabeled data without predefined outputs.

What is Unsupervised Learning?

In unsupervised learning, the input data has no associated labels. The goal is to explore the data’s inherent structure, group similar data points, or reduce the data’s dimensionality.

Common Types of Unsupervised Learning

• Clustering: Groups similar data points into clusters.

 Example: Customer segmentation.  

• Dimensionality Reduction: Reduces the number of variables while preserving important information.

 Example: Principal Component Analysis (PCA).  

• Association Rule Learning: Finds interesting relationships or patterns in large datasets.

 Example: Market basket analysis.

How Unsupervised Learning Works

1. The model receives unlabeled data. 2. It uses similarity or statistical methods to find patterns. 3. Results may be clusters, components, or association rules depending on the technique.

Applications of Unsupervised Learning

• Market segmentation.
• Anomaly detection.
• Data compression.
• Recommender systems.
• Visualization of complex data.

Challenges of Unsupervised Learning

• No clear measure of accuracy since no labels are available.
• Defining meaningful similarity measures.
• Determining the number of clusters or components.
• Interpreting the discovered patterns.

Related Pages

• Clustering
• Dimensionality Reduction
• Association Rule Learning
• K-Means Clustering
• PCA (Principal Component Analysis)
• Evaluation Metrics

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