1. A method of detecting spam email messages comprising: using a computer to perform steps comprising: collecting attributes of a web site to which an email address was submitted; collecting attributes of an email message sent to the email address; identifying a degree of correlation between at least one of the collected attributes of the web site and at least one of the collected attributes of the email message, the identifying comprising using a classifier to analyze the at least one collected attribute of the web site and the at least one collected attribute of the email message, wherein the analysis is based at least in part on a plurality of weights describing different values that represent the relative importances of the collected attributes of the web site and email message, wherein the classifier is generated by training on training data describing attributes of training web sites to which email addresses were submitted and legitimate emails received responsive to the submissions of the email addresses to the training web sites, generating the classifier comprising: generating feature vectors from the training data, the feature vectors having features describing the attributes of the training web sites and having features describing the attributes of the legitimate emails received responsive to the submissions of the email addresses to the training web sites; and training the classifier using the feature vectors, the training causing the classifier to learn weights describing relative importances of the features in recognizing when email messages are received in response to email addresses submitted to web sites; and determining whether the email message is spam responsive at least in part to the degree of correlation, a stronger correlation indicating a decreased likelihood that the email message is spam.
1. A method of detecting spam email messages comprising: using a computer to perform steps comprising: collecting attributes of a web site to which an email address was submitted; collecting attributes of an email message sent to the email address; identifying a degree of correlation between at least one of the collected attributes of the web site and at least one of the collected attributes of the email message, the identifying comprising using a classifier to analyze the at least one collected attribute of the web site and the at least one collected attribute of the email message, wherein the analysis is based at least in part on a plurality of weights describing different values that represent the relative importances of the collected attributes of the web site and email message, wherein the classifier is generated by training on training data describing attributes of training web sites to which email addresses were submitted and legitimate emails received responsive to the submissions of the email addresses to the training web sites, generating the classifier comprising: generating feature vectors from the training data, the feature vectors having features describing the attributes of the training web sites and having features describing the attributes of the legitimate emails received responsive to the submissions of the email addresses to the training web sites; and training the classifier using the feature vectors, the training causing the classifier to learn weights describing relative importances of the features in recognizing when email messages are received in response to email addresses submitted to web sites; and determining whether the email message is spam responsive at least in part to the degree of correlation, a stronger correlation indicating a decreased likelihood that the email message is spam. 5. The method of claim 1 , wherein collecting attributes of an email message sent to the email address comprises: collecting one or more primary attributes describing the email message; and collecting one or more secondary attributes derived from the primary attributes.