A comparative study of unsupervised image clustering systems
| Autor: | Chiraz Jlassi, Najet Arous, Safa Bettoumi |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Information Systems and Management
Computer science Applied Mathematics Unstructured data Mutual information Automatic learning computer.software_genre Fuzzy logic Hierarchical clustering Image (mathematics) ComputingMethodologies_PATTERNRECOGNITION Data mining Cluster analysis computer Density based clustering Information Systems |
| Zdroj: | International Journal of Data Analysis Techniques and Strategies. 11:197 |
| ISSN: | 1755-8069 1755-8050 |
| DOI: | 10.1504/ijdats.2019.10022548 |
| Popis: | The purpose of clustering algorithms is to give sense and extract value from large sets of structured and unstructured data. Thus, clustering is present in all science areas that use automatic learning. Therefore, we present in this paper a comparative study and an evaluation of different clustering methods proposed in the literature such as prototype based clustering, fuzzy and probabilistic clustering, hierarchical clustering and density based clustering. We present also an analysis of advantages and disadvantages of these clustering methods based essentially on experimentation. Extensive experiments are conducted on three real-world high dimensional datasets to evaluate the potential and the effectiveness of seven well-known methods in terms of accuracy, purity and normalised mutual information. |
| Databáze: | OpenAIRE |
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