This paper presents a methodology for the characterization of spamming strategies based on the identification of spam campaigns. To deeply understand how spammers abuse network resources and obfuscate their messages, an aggregated analysis of spam messages is not enough. Grouping spam messages into campaigns is important to unveil behaviors that cannot be noticed when looking at the whole set of spams collected. We propose a spam identification technique based on a frequent pattern tree, which naturally captures the invariants on message content and detect campaigns that differ only due to obfuscated fragments. After that, we characterize these campaigns both in terms of content obfuscation and exploitation of network resources. Our methodology includes the use of attribute association analysis: by applying an association rule mining algorithm, we were able to determine cooccurrence of campaign attributes that unveil different spamming strategies. In particular, we found strong relations between the origin of the spam and how it abused the network, and also between operating systems and types of abuse.