Spam Dataset for classification

1. Title:  SPAM E-mail Database

2. Sources:
 (a) Creators: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt
      Hewlett-Packard Labs, 1501 Page Mill Rd., Palo Alto, CA 94304
 (b) Donor: George Forman (gforman at nospam hpl.hp.com)  650-857-7835
 (c) Generated: June-July 1999

3. Past Usage:
 (a) Hewlett-Packard Internal-only Technical Report. External forthcoming.
 (b) Determine whether a given email is spam or not.
 (c) ~7% misclassification error.
     False positives (marking good mail as spam) are very undesirable.
     If we insist on zero false positives in the training/testing set,
     20-25% of the spam passed through the filter.

4. Relevant Information:
      The "spam" concept is diverse: advertisements for products/web
      sites, make money fast schemes, chain letters, pornography...
 Our collection of spam e-mails came from our postmaster and
 individuals who had filed spam.  Our collection of non-spam
 e-mails came from filed work and personal e-mails, and hence
 the word 'george' and the area code '650' are indicators of
 non-spam.  These are useful when constructing a personalized
 spam filter.  One would either have to blind such non-spam
 indicators or get a very wide collection of non-spam to
 generate a general purpose spam filter.

      For background on spam:
      Cranor, Lorrie F., LaMacchia, Brian A.  Spam!
      Communications of the ACM, 41(8):74-83, 1998.

http://www.ics.uci.edu/~mlearn/databases/spambase/