GA-Based Membrane Evolutionary Algorithm for Ensemble Clustering.

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 perform better than several state-of-the-art techniques on six real-world UCI data sets.

journal_name

Comput Intell Neurosci

authors

Wang Y,Liu X,Xiang L

doi

10.1155/2017/4367342

subject

Has Abstract

pub_date

2017-01-01 00:00:00

pages

4367342

eissn

1687-5265

issn

1687-5273

journal_volume

2017

pub_type

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