Artículo
An unsupervised learning algorithm for membrane computing
Autor/es | Peng, Hong
Wang, Jun Pérez Jiménez, Mario de Jesús Riscos Núñez, Agustín |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2015 |
Fecha de depósito | 2019-03-28 |
Publicado en |
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Resumen | This paper focuses on the unsupervised learning problem within membrane computing,
and proposes an innovative solution inspired by membrane computing techniques, the
fuzzy membrane clustering algorithm. An evolution–co ... This paper focuses on the unsupervised learning problem within membrane computing, and proposes an innovative solution inspired by membrane computing techniques, the fuzzy membrane clustering algorithm. An evolution–communication P system with nested membrane structure is the core component of the algorithm. The feasible cluster centers are represented by means of objects, and three types of membranes are considered: evolution, local store, and global store. Based on the designed membrane structure and the inherent communication mechanism, a modified differential evolution mechanism is developed to evolve the objects in the system. Under the control of the evolution–communication mechanism of the P system, the proposed fuzzy clustering algorithm achieves good fuzzy partitioning for a data set. The proposed fuzzy clustering algorithm is compared to three recently-developed and two classical clustering algorithms for five artificial and five real-life data sets. |
Identificador del proyecto | 61170030
61472328 Z2012025 Z2012031 2013GZX0155 |
Cita | Peng, H., Wang, J., Pérez Jiménez, M.d.J. y Riscos Núñez, A. (2015). An unsupervised learning algorithm for membrane computing. Information Sciences, 304 (may 2015), 80-91. |
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