| Autor: |
Joaquín Pérez-Ortega, Sandra Silvia Roblero-Aguilar, Nelva Nely Almanza-Ortega, Juan Frausto Solís, Crispín Zavala-Díaz, Yasmín Hernández, Vanesa Landero-Nájera |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Axioms, Vol 11, Iss 8, p 377 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2075-1680 |
| DOI: |
10.3390/axioms11080377 |
| Popis: |
A hybrid variant of the Fuzzy C-Means and K-Means algorithms is proposed to solve large datasets such as those presented in Big Data. The Fuzzy C-Means algorithm is sensitive to the initial values of the membership matrix. Therefore, a special configuration of the matrix can accelerate the convergence of the algorithm. In this sense, a new approach is proposed, which we call Hybrid OK-Means Fuzzy C-Means (HOFCM), and it optimizes the values of the membership matrix parameter. This approach consists of three steps: (a) generate a set of n solutions of an x dataset, applying a variant of the K-Means algorithm; (b) select the best solution as the basis for generating the optimized membership matrix; (c) resolve the x dataset with Fuzzy C-Means. The experimental results with four real datasets and one synthetic dataset show that HOFCM reduces the time by up to 93.94% compared to the average time of the standard Fuzzy C-Means. It is highlighted that the quality of the solution was reduced by 2.51% in the worst case. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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