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中文摘要：
现有的文本聚类方法难以正确识别和描述文本的主题，从而难以实现按照主题对文本进行聚类。本文提出了一种新的基于主题的文本聚类方法：LFIC。该方法能够准确识别文本主题并根据文本的主题对其进行聚类。本方法定义和抽取了“主题元素”，并利用其进行基本类索引。同时还整合利用了语言学特征。实验表明，LFIC的聚类准确率达到94．66％，优于几种传统聚类方法。
英文摘要：
Few of the existing document clustering methods can detect or describe document topics properly, which makes it difficult to conduct clustering based on topics. In this paper, we introduce a novel topical document clustering method called Linguistic Features Indexing Clustering （LFIC）, which can identify topics accurately and cluster documents according to these topics. In LFIC, ＂topic elements＂ are defined and extracted for indexing base clusters, Additionally, linguistic features are exploited. Experimental results show that LFIC can gain a higher precision （94. 66 %） than some widely used traditional clustering methods.
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