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Approximated Cluster Analysis Models for Big Data

Student: Ivan Shabrashin

Supervisor: Fuad T. Aleskerov

Faculty: Faculty of Computer Science

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2020

The purpose of the work is to develop special methods for the evaluation accuracy of three pattern clustering algorithms for big data. There is a comparison of the results of these algorithms’ work on similar datasets randomly generated. The structure of the work is the following: definitions, an overview of existing clustering algorithms, pattern analysis, and evaluation of pattern clustering algorithms using developed methods. As a result, the accuracy of all three algorithms was evaluated.

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