Cluster Analysis on Base of Multi-objective Genetic Algorithm

Student: Kovalchuk Dmitrii

Educational Programme: Business Informatics (Bachelor)

Year of Graduation: 2018

This bachelor thesis is an attempt to create a model for clustering data using a multi-objective genetic algorithm. The general purpose is to realise an algorithm that will cluster any table data more efficient or at least not much worse than some conventional clustering algorithms like K-Means, Ward, EM, etc. The topic belongs to study of machine learning and intersects with linear algebra, calculus, programming and biology. The algorithm itself could be a powerful tool for the data scientists that work with clustering data and for the data mining researches who want to investigate some patterns in data.

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