【摘要】 提出一种新的动态模糊聚类的方法,针对传统的模糊聚类需要预先确定聚类数的问题,提出采用动态自组织映射神经网络来确定聚类数,并通过文本向量空间模型和TF?IDF方法来确定文本的特征向量,再将动态自组织映射神经网络得到的聚类数,用模糊C均值算法(FCM)函数处理,得到聚类的结果。该算法同仅用动态自组织映射神经网络算法的运行结果相比,具有运行聚类结果精度高的优点,模糊聚类更适合处理语义的多样性和文本归属的模糊性,实验验证了算法的有效性。更多还原

【Abstract】 A novel approach is presented.The main defect of traditional methods of fuzzy clustering is to known the number of clustering in advance.This paper applies the dynamic self-organizing maps algorithm to determining the number of clustering.The text eigenvector is acquired based on the vector space model(VSM) and TF?IDF method.The result of clustering is attained by fuzzy C mean algorithm(FCM).The number of clustering acquired by the dynamic self-organizing maps is introduced into the fuzzy C mean algorithm(FCM).Compared to the dynamic self-organizing maps algorithm,the present algorithm possesses higher precision.The fuzzy clustering is suitable for dealing with the semantic variety and complexity.The example demonstrates the effectiveness of the present algorithm.更多还原