1. An unsupervised learning algorithm for membrane computing.
- Author
-
Peng, Hong, Wang, Jun, Pérez-Jiménez, Mario J., and Riscos-Núñez, Agustín
- Subjects
- *
SUPERVISED learning , *COMPUTER algorithms , *FUZZY clustering technique , *DATA analysis , *COMPUTER networks - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF