Cluster analysis is a number of different algorithms and techniques for grouping objects sharing similar characteristics. Researchers in many areas face problems such as how to organize observed data into meaningful structures. In other words cluster analysis is an exploratory data analysis tool that focuses at putting objects into groups of the same kind based on measures of similarity, such as distance, defining association of objects within groups, and disassociation between objects of different groups. Given the above, cluster analysis can be used to discover structures in data without providing an interpretation. In other words, cluster analysis simply discovers structures in data without explaining why they exist. It is the user’s task to interpret the resulting groups or clusters, attaching to them meaningful descriptions.
This course mainly focuses on reviewing those algorithms and techniques that lead to produce clusters for different data types taken on real life and brought to you as examples.