Definition: Clustering is a technique used to group similar data points together.

Detailed Description:

Clustering algorithms identify natural groupings or clusters within a dataset based on similarity measures. These clusters can be used to discover patterns, anomalies, or relationships within the data.

Common clustering algorithms include:

• K-means clustering: A popular algorithm that partitions data into K clusters.
• Hierarchical clustering: Creates a hierarchy of clusters, from small to large.
• Density-based clustering: Identifies clusters based on dense regions of data points.

Clustering is used in various applications, such as:

• Customer segmentation: Grouping customers based on similar characteristics.
• Image segmentation: Dividing images into different regions.
• Anomaly detection: Identifying outliers in data.

Related Terms:

• Data Mining
• Unsupervised Learning
• K-means Clustering
• Hierarchical Clustering
• Density-based Clustering

FAQs:

1. What is the difference between clustering and classification?

   • Clustering is an unsupervised learning technique that groups data points into similar clusters, while classification is a supervised learning technique that predicts the category or class of a data point.

2. How is clustering used in customer segmentation?

   • Clustering can help identify customer segments based on demographics, purchasing behavior, or other relevant factors.

3. What are the challenges of clustering?

   • Choosing the right clustering algorithm, determining the optimal number of clusters, and dealing with noisy or high-dimensional data.