Cluster analysis

Cluster analysis refers to unsupervised machine learning techniques for grouping unlabeled data points. Key aspects:

• Algorithms organize objects into clusters based on similarity.
• Objects within a cluster are more closely related than between clusters.
• Useful for discovering structures in data without predefined categories.
• Reveals associations, patterns, and distributions in data.
• Applications include customer segmentation, social network analysis, bioinformatics.

Major approaches include:

• Connectivity-based clustering - Hierarchical clustering creates a dendrogram tree.
• Centroid-based clustering - k-means, k-medoids group points around centroids.
• Distribution-based clustering - Expectation-maximization finds statistical distributions.

Challenges include determining optimal number of clusters, handling outliers, and defining appropriate similarity measures for data types.

Cluster analysis provides crucial tools for exploratory data analysis and unsupervised machine learning. It has widespread applications in data mining, pattern recognition, image analysis, and more.

See also:

• Unsupervised learning
• Statistical classification
• Data mining
• Machine learning