Adding Semantics to Email Clustering

This paper presents a novel algorithm to cluster emails according to their contents and the sentence styles of their subject lines. In our algorithm, natural language processing techniques and frequent itemset mining techniques are utilized to automatically generate meaningful generalized sentence patterns (GSPs) from subjects of emails. Then we put forward a novel unsupervised approach which treats GSPs as pseudo class labels and conduct email clustering in a supervised manner, although no human labeling is involved. Our proposed algorithm is not only expected to improve the clustering performance, it can also provide meaningful descriptions of the resulted clusters by the GSPs. Experimental results on open dataset (Enron email dataset) and a personal email dataset collected by ourselves demonstrate that the proposed algorithm outperforms the K-means algorithm in

• Type: Conference
• Year: 2006
• Where: ICDM
• Authors: Hua Li, Dou Shen, Benyu Zhang, Zheng Chen, Qiang Yang
• Keywords: Algorithm, Data Mining, Email Dataset, ICDM 2006, Meaningful Generalized Sentence