Data clustering is the process of organizing objects into groups or clusters based on similarity, where objects within the same cluster are more similar to each other than to those in other clusters.

Key Components of Clustering

1. Similarity Measures

• Define proximity between pairs of objects
• Calculate relationships between data points
• Help determine cluster membership

2. Clustering Algorithms

Two main approaches:

• Hierarchical: Builds nested clusters
• Partition-based: Divides data into non-overlapping clusters

3. Algorithm Selection Criteria

• Type and size of data
• Hardware capabilities
• Software requirements
• Specific application needs

Applications

Data clustering finds use in various fields:

• Exploratory pattern analysis
• Decision-making processes
• Machine learning applications
• Data mining
• Pattern classification
• Document retrieval
• Image segmentation