Comparison Between Gustafson-Kessel Clustering and Fuzzy C-Means Clustering

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Insinööritieteiden korkeakoulu | Bachelor's thesis
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Date

2025-08-31

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Major/Subject

Computational Engineering

Mcode

ENG3082

Degree programme

Aalto Bachelor’s Programme in Science and Technology

Language

en

Pages

26

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Abstract

Clustering is an important technique utilized in unsupervised learning that groups data points based on similarity. This grouping facilitates the discovery of hidden trends in unexplored data. Nonetheless, many classical algorithms are insufficient for real-world data, since they cannot create multiclass classifications or detect overlapping clusters. To counter the problem, existing clustering methods were combined with fuzzy logic, leading to more versatile algorithms, such as fuzzy c-means and Gustafson-Kessel. Therefore, this thesis analyzes the performance of the standard Gustafson-Kessel algorithm and one of its variants and compares it to fuzzy c-means on the Iris dataset. Furthermore, it describes the implementation of both the standard and the modified Gustafson-Kessel algorithms in the programming language Julia. The results of the study indicate that the modification is required for the stability of Gustafson-Kessel for datasets with few data points or linear clusters. Nonetheless, its usage on general datasets significantly decreased the average performance compared to the standard version and even fuzzy c-means.

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Supervisor

St-Pierre, Luc

Thesis advisor

Ferranti, Luca

Keywords

clustering algorithms, fuzzy c-means, Gustafson-Kessel, Iris dataset

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https://urn.fi/URN:NBN:fi:aalto-202510218382

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[kand] Insinööritieteiden korkeakoulu / ENG