Structural Clustering of Machine-Generated Mail
Noa Avigdor-Elgrabli, Mark Cwalinski, Dotan Di Castro, Iftah Gamzu, Irena Grabovitch-Zuyev, Liane Lewin-Eytan, Yoelle Maarek · 2016
Several recent studies have presented different approaches for clustering and classifying machine-generated mail based on email headers. We propose to expand these approaches by considering email message bodies. We argue that our approach can help increase coverage and precision in several tasks, and is especially critical for mail extraction. We remind that mail extraction supports a variety of mail mining applications such as ad re-targeting, mail search, and mail summarization. We introduce new structural clustering methods that leverage the HTML structure that is common to messages generated by a same mass-sender script. We discuss how such structural clustering can be conducted at different levels of granularity, using either strict or flexible matching constraints, depending on the use cases.