Cluster Analysis

Group objects in clusters according to similar characteristics
The model is trained on instances whose category membership is known, and then uses the same algorithm to determine the category for the new observation.

Credit scoring is one of the most well-known examples of statistical classification. A credit score is a metric based on the analysis of credit files of a person to prove their credit-worthiness. It is used to classify applicants for credit into 'good' and 'bad' risk classes. Credit scoring is used by banks, government departments, and insurance, mobile phone, digital finance, and other companies.
Cluster Analysis allows you to break down a large dataset into smaller groups
It becomes easy to identify patterns among objects. For example, in marketing, it could lead to a better understanding of a target audience
It is used to build a clear structure that enables easier decision making
Clustering in Social Media Manipulation
In research for NATO's StratCOM, we studied the market for social-media manipulation using metrics such as the selling of likes, comments, shares, accounts, and followers. Using cluster analysis, we studied a host of factors and defined three categories of social media manipulation that became subjects of further research.
Cluster analysis to predict ICO proceeds
All of the 1200 ICO projects we examined were divided into 6 groups, with the help of clustering. This method helped us to find the dependencies among the factors that most influence ICO fundraising.
Clustering in Healthcare
In a comparative study of two Cesarean techniques, the same methods can't be applied to different types of patients. In this study, clusters of patients were identified at the beginning so that further research on homogeneous groups could be conducted.
Cluster analysis to predict wind turbine power
In this case, clustering was used to recover missing data in a database and to fill in typical cluster values.
Clustering algorithm to identify topics and user types in social media posts
In research into the effects of banning VKontakte in Ukraine, we used clustering in our analysis of post topics. This helped us conclude that 'Pro-Russian propaganda' notably increased while the share of 'Ukrainian news' decreased.

In NATO StratCom research on the virtual Russian world of the Baltics, we analyzed more than 92 million posts and millions of user profiles. Based on a cluster analysis of user profiles, it was possible to identify four types of ideological users: Writers, Distributors, Readers, and Members of the Active Reserve. As a result, we managed to study them more accurately.

In research conducted with Internews Ukraine on the Ukrainian presidential elections, our script determined 152 topic clusters in more than 9K texts. Thanks to clustering analysis, we identified 40 users who exhibited strange activity on VKontakte and appeared to be social bots.
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