TGFCM:基于模糊聚类的中文文本挖掘的新方法

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。

TGFCM: A Novel Approach of Chinese Text Mining Based on Fuzzy Clustering

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.