基于概念格的Web文本聚类

Web文本聚类大多是基于空间向量文本表示模型的，它没有考虑特征词之间的语义关系，并且特征词的维数非常高，造成文本语义信息的损失和时间复杂度的增加。把文本作为对象，文本中的特征词作为对应的属性，形成了基于文本的形式背景，从中提取概念来表示文本并度量文本之间的相似度，从而降低了特征词的维数，减少了计算的复杂度，取得了良好的聚类结果。

Web text clustering based on concept lattice

Web text clustering are mostly based on space vector text express model,the semantics relation of the terms in the text is not considered in this method and the dimension of the terms is very high,which results in the losing of text semantics and the increase of time complexity.The text is considered as object in this paper,and the term of text is abstract as the corresponding attribute.Therefore,a formal context is formed based on text.To express text and measure the similarity the authors extract the concept from formal context.Thus,the dimension of term is reduced,and the complexity of computation is decreased too.Theoretical analysis shows that the method of clustering is effective.