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Assessment metrics for clustering algorithms

Angelopoulos Vasileios

Πλήρης Εγγραφή

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Περίληψη

In the first part of the Thesis, the theoretical background will be provided for Artificial Intelligence, Machine Learning, Supervised and Unsupervised Learning and Clustering in order to provide the basis for understanding the next chapters. In the second chapter, the theoretical and mathematical background is provided for each clustering algorithm that will be implemented. In the third chapter of the Thesis the different clustering assessment metrics are discussed and their mathematical background is presented. In the fourth chapter the basic steps of the implementation that will be carried out in order to provide the necessary outputs will be described. The intended output/result of the Thesis is the development of a software script in Python capable to take different datasets as an input, implement different clustering methods and export the clustering performance metrics. The correct operation of the developed script is shown in the final chapter where an example dataset is used to showcase the capabilities of the script by presenting the results/outputs of the script along with commentary. A manual covering the basic elements of the script can be found in the same chapter.

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