Name
Abstract | ||
|---|---|---|
Ensemble clustering can improve the generalization ability of a single clustering algorithm and generate a more robust clustering result by integrating multiple base clusterings, so it becomes the focus of current clustering research. Ensemble clustering aims at finding a consensus partition which agrees as much as possible with base clusterings. Genetic algorithm is a highly parallel, stochastic, and adaptive search algorithm developed from the natural selection and evolutionary mechanism of biology. In this paper, an improved genetic algorithm is designed by improving the coding of chromosome. A new membrane evolutionary algorithm is constructed by using genetic mechanisms as evolution rules and combines with the communication mechanism of cell-like P system. The proposed algorithm is used to optimize the base clusterings and find the optimal chromosome as the final ensemble clustering result. The global optimization ability of the genetic algorithm and the rapid convergence of the membrane system make membrane evolutionary algorithm performbetter than several state-of-the-art techniques on six real-world UCI data sets. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1155/2017/4367342 | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE |
Field | DocType | Volume |
Canopy clustering algorithm,Fuzzy clustering,CURE data clustering algorithm,Pattern recognition,Correlation clustering,Computer science,Consensus clustering,Constrained clustering,Artificial intelligence,Cluster analysis,Population-based incremental learning,Machine learning | Journal | 2017 |
ISSN | Citations | PageRank |
1687-5265 | 1 | 0.35 |
References | Authors | |
19 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanhua Wang | 1 | 47 | 6.35 |
| Xi-Yu Liu | 2 | 20 | 12.35 |
| Lai-Sheng Xiang | 3 | 2 | 3.07 |