Cluster Discovery

Cluster discovery, also known as cluster analysis, is a technique in data mining and data warehousing that involves grouping similar data points together into clusters. The goal of cluster discovery is to identify patterns and relationships within the data that may not be immediately apparent, and to use these insights to make more informed business decisions.

There are several methods for performing cluster discovery, including hierarchical clustering, k-means clustering, and density-based clustering. Each method has its own strengths and weaknesses, and the choice of method will depend on the specific needs of the data mining project.

Cluster discovery is a key component of data mining and data warehousing because it allows businesses to gain insights into their data that they may not have been able to obtain through other means. By grouping similar data points together, businesses can identify trends, patterns, and relationships that can help them make better decisions about everything from product development to marketing strategies.