DOI: 10.4018/978-1-68318-016-6.ch008
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Cluster analysis, which we approach in this chapter, is the task of grouping a set of objects in such a way that objects in the same group or cluster are more similar to each other than to those in other groups or clusters. It is a common technique for statistical data analysis. Cluster analysis can be achieved by various algorithms that might differ significantly. Therefore, cluster analysis as such is not a trivial task. It is an interactive multi-objective optimization that involves trial and error. Therefore, in cluster analysis, the clustering of subjects or variables are made from similarity measures or dissimilarity (distance) between two subjects initially, and later between two clusters. These groups can be done using hierarchical or non-hierarchical techniques.
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R Vs. Python

In this chapter, cluster analysis will be, once more, also conducted also in R language and Python. First, a brief theoretical summary will be necessary for the reader to understand the concepts regarding clusterization.

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