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Cluster analysis

What is cluster analysis?

Cluster analysis is a statistical classification technique in which a set of objects or points with similar characteristics are grouped into clusters. It involves a number of different algorithms and methods, all of which are used to group objects of a similar nature into appropriate categories. The goal is to organize observed data into meaningful structures in order to gain further insight.

What was cluster analysis used for?

To identify differences or similarities between groups of objects and describe them in graphical or algebraic form to gain a better understanding of a particular area.

What are the challenges of cluster analysis?

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  • Associated subjectivity
  • Difficulties in validation

When are cluster analyses used?

They are particularly useful for finding interesting insights in data and visualizing information.

Examples of useful uses of cluster analysis:

Cluster analysis is often applied to very simple things without us knowing it, such as meaningful food groupings in the supermarket or a group of people eating together in a restaurant.
Other examples:
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  • In marketing: for customer segmentation
  • In social networks: For identifying communities


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To the article


What is meant by cluster analysis?

Cluster analysis, or clustering, is the task of grouping a set of objects so that objects in the same group (called a cluster) are more similar to each other (in some sense) than objects in other groups (clusters). ... Clustering can therefore be formulated as an optimization problem with multiple objectives.

Why do we use cluster analysis?

It is often used as a data analysis technique to identify interesting patterns in data, such as groups of customers based on their behavior. ... Clustering is an unsupervised problem of finding natural groups in the feature space of input data.

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