Clustering

Clustering

Clustering is an unsupervised machine learning technique that groups data points into clusters such that points in the same cluster are more similar to each other than to those in other clusters.

What is Clustering?

Unlike supervised learning, clustering does not use labeled data. The goal is to find natural groupings or patterns within the data based on similarity or distance measures.

Types of Clustering

• Partitioning Methods: Divide data into a set number of clusters.

 Example: K-Means clustering.  

• Hierarchical Clustering: Builds a tree of clusters by merging or splitting them.
• Density-Based Clustering: Groups points based on density of data points in regions.

 Example: DBSCAN.  

• Model-Based Clustering: Assumes data is generated by a mixture of underlying probability distributions.

How Clustering Works

1. Select the number of clusters or let the algorithm determine it. 2. Calculate similarity/distance between data points (e.g., Euclidean distance). 3. Assign points to clusters based on similarity criteria. 4. Update clusters iteratively until stable.

Popular Clustering Algorithms

• K-Means
• Hierarchical Agglomerative Clustering
• DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
• Gaussian Mixture Models (GMM)

Applications of Clustering

• Customer segmentation in marketing
• Image segmentation in computer vision
• Anomaly detection
• Document or text grouping
• Bioinformatics for gene expression analysis

Challenges in Clustering

• Choosing the right number of clusters.
• Handling noisy data and outliers.
• Defining appropriate similarity measures.
• Computational complexity for large datasets.

Related Pages

• Unsupervised Learning
• Classification
• K-Means Algorithm
• DBSCAN
• Dimensionality Reduction
• Evaluation Metrics

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