蚁群-遗传融合的文本聚类算法

针对蚁群算法容易出现停滞现象而不能对解空间进行全面搜索的问题,提出了一种蚁群-遗传融合的文本聚类算法.该算法将影响蚁群算法性能的4个参数作为遗传算法中的染色体进行编码,基于此又设计出相应的适应度函数以及选择交叉变异算子,通过多次迭代找出最优的参数组合,并将其应用到文本聚类问题上.经与经典的k均值聚类算法、基本的蚁群聚类算法的仿真比较,结果表明所提出算法的聚类效果更好,在3个测试集上的F度量值要比k均值聚类算法分别提高5.69%、48.60%、69.60%,所以更适合于处理较大规模的数据集.

Text Clustering Based on Fusion of Ant Colony and Genetic Algorithms

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 fusion of ant colony and genetic algorithm is proposed.The four parameters which influence the performance of 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 iterations,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 larger datasets.