Text clustering based on fusion of ant colony and genetic algorithms

Yun ZHANG, Boqin FENG, Shouqiang MA, Lianmeng LIU

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PDF(145 KB)
Front. Electr. Electron. Eng. ›› 2009, Vol. 4 ›› Issue (1) : 15-19. DOI: 10.1007/s11460-009-0019-9
RESEARCH ARTICLE
RESEARCH ARTICLE

Text clustering based on fusion of ant colony and genetic algorithms

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Abstract

Focusing on the problem that the ant colony algorithm gets into stagnation easily and cannot fully search in solution space, a text clustering approach based on the fusion of the ant colony and genetic algorithms is proposed. The four parameters that influence the performance of the ant colony algorithm are encoded as chromosomes, thereby the fitness function, selection, crossover and mutation operator are designed to find the combination of optimal parameters through a number of iteration, and then it is applied to text clustering. The simulation results show that compared with the classical k-means clustering and the basic ant colony clustering algorithm, the proposed algorithm has better performance and the value of F-Measure is enhanced by 5.69%, 48.60% and 69.60%, respectively, in 3 test data sets. Therefore, it is more suitable for processing a larger dataset.

Keywords

ant colony clustering / genetic algorithm / fusion / text clustering

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Yun ZHANG, Boqin FENG, Shouqiang MA, Lianmeng LIU. Text clustering based on fusion of ant colony and genetic algorithms. Front Elect Electr Eng Chin, 2009, 4(1): 15‒19 https://doi.org/10.1007/s11460-009-0019-9

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Acknowledgements

This work was supported by the Hi-Tech Research and Development Program of China (No. 2006AA01Z210).

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
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