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Several features existed in Chinese texts result in technologic bottleneck in Chinese text mining, at present the results of Chinese text clustering obtained by traditional methods are not very satisfactory. In this paper, we propose the text clustering method by the English texts clustering method called as Text Clustering via Particle Swarm Optimizer (TCPSO) to solve the Chinese text clustering...
This paper presents a novel stochastic approach called the simulated annealing-artificial fish swarm algorithm (SA-AFSA) for solving some multimodal problems. The proposed algorithm incorporates the simulated annealing (SA) into artificial fish swarm algorithm (AFSA) to improve the performance of the AFSA. The hybrid algorithm has the following features: the hybrid algorithm maintains 1) the strong...
Artificial fish swarm algorithm (AFSA) is a kind of swarm intelligence algorithms, which has the features of not strict to parameter setting, insensitive to initial values, strong robustness and so on. But the precision can not be very high and artificial fish (AF) often suffers the problem of being trapped in local optima. Especially when the objective function is a multimodel function, this problem...
An improved artificial fish swarm algorithm (IAFSA) is proposed, and its complexity is much less than the original algorithm (AFSA) because of a new proposed fish behavior. Based on IAFSA, two novel algorithms for data clustering are presented. One is the improved artificial fish swarm clustering (IAFSC) algorithm, the other is a hybrid fuzzy clustering algorithm that incorporates the fuzzy c-means...
Artificial fish swarm algorithm (AFSA) is a novel intelligent optimization algorithm. It has many advantages, such as good robustness, global search ability, tolerance of parameter setting, and it is also proved to be insensitive to initial values. However, it has some weaknesses as low optimizing precision and low convergence speed in the later period of the optimization. In this paper, an improved...
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