What is cluster analysis? Which steps are involved in cluster analysis? Give illustration

Cluster analysis is a statistical technique used to group a set of objects in such a way that objects in the same group (cluster) are more similar to each other than to those in other groups (clusters). This technique is commonly used in various fields, such as marketing, biology, computer science, and psychology, to identify patterns, relationships, and structures in data.

There are several methods for performing cluster analysis, including hierarchical clustering, k-means clustering, and density-based clustering. In hierarchical clustering, the data is recursively partitioned into clusters using a tree-like structure. The k-means clustering algorithm groups the data into k number of clusters based on the mean distance between the objects in the same group. Density-based clustering, such as DBSCAN, identifies clusters by identifying regions of high-density points.

Cluster analysis has many practical applications. For example, it can be used to segment customers based on their buying habits or preferences, to identify different subpopulations within a biological sample, or to group documents based on their content. By identifying these groups, __________ _______ _________ ________ _____ ________ ________ ___ ______ _______.
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