【摘要】 为同步选择具有相关特征的数据聚类数量,提出一种基于引力搜索机制的聚类和特征选择算法。设计一种代理表示策略实现聚类中心和特征数量的编码;提出一种动态临界值方法决定聚类和特征数量,通过代理适应度的不断评估寻找最优聚类量和相关特征;分析算法的时间复杂度,通过8个经典数据集测试算法性能,并与7种常规数据聚类算法作对比。实验结果表明,所提算法在聚类和特征数量选择上具有更高的准确率。更多还原

【Abstract】 To synchronously select the number of data clusters along with relevant features,a clustering and feature selection algorithm based on gravitational search mechanism was proposed.An agent representation strategy was designed to encode the clustering center and the number of features.A dynamic threshold method was proposed to determine the clustering and the number of features,and the optimal number of clusters and the relevant features were found according to the continuous evaluation of the agent fitness.The time complexity of the proposed algorithm was analyzed,the performance of the algorithm was tested with eight classical data sets and compared with seven conventional data clustering algorithms.Experimental results show that the proposed algorithm has higher accuracy in clustering and feature number selection.更多还原