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http://hdl.handle.net/10609/97627
Title: | Fuzzy C-means and clustering algorithms: a comparative study |
Author: | García Domingo, Victor |
Tutor: | Nunez do Rio, Joan M |
Others: | Ventura, Carles |
Abstract: | Clustering is a technique that groups observations in a dataset based on the distance to the centre of the clusters. One of the first clustering algorithms was K-Means (KM), which is especially accurate at recognising well-separated clusters. Afterwards, Fuzzy C-Means (FCM) was formulated to improve the accuracy of KM with datasets containing overlapping clusters. Since then, other derivatives of FCM have been developed to improve it: Gustafson Kessel Fuzzy C-Means (GKFCM) performs better for non-spherical clusters, Fuzzy C-Means++ (FCM++) and Suppressed-Fuzzy C-Means (S-FCM) improve FCM's efficiency and Possibilistic C-Means (PCM) is more accurate for datasets with noise and outliers. In this project, I have compared KM, FCM, GKFCM, FCM++, S-FCM and PCM to check how each evolution has improved its predecessor. This comparison is centralised around FCM. I have validated parameters such as computational efficiency, performance and accuracy. I have found that, among all the algorithms, FCM has the best performance for datasets with overlapping clusters, even though S-FCM improves its computational efficiency. Also, KM is the most efficient algorithm and GKFCM performs well with non-spherical clusters. However, it is less accurate. Finally, PCM has not shown any advantage over FCM. This project is a starter point for future investigations of the conditions under which every algorithm works better. Most of the datasets used here are synthetic datasets, based on near-ideal characteristics. Nevertheless, real-world datasets are expected to have more complex structures for which the choice of algorithms require a more thorough investigation. |
Keywords: | clustering Fuzzy C-Means algorithms |
Document type: | info:eu-repo/semantics/bachelorThesis |
Issue Date: | Jun-2019 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Bachelor thesis, research projects, etc. |
Files in This Item:
File | Description | Size | Format | |
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vgarciadomiTFG0619video.mp4 | Video of TFG | 49,64 MB | MP4 | View/Open |
vgarciadomiTFG0619memory.pdf | Memory of TFG | 1,56 MB | Adobe PDF | View/Open |
vgarciadomiTFG0619presentation.pdf | Presentation of TFG | 713,65 kB | Adobe PDF | View/Open |
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