Good books covering data preprocessing and outlier detection techniques As the title goes, does anyone know of a good, up to date book that covers data preprocessing in general and especially outlier detection techniques?
The book doesn't need to be focusing exclusively on that, but it should deal with the aforementioned topics exhaustively - I wouldn't be happy with something that's a starting point and quotes a list of papers, explanations of the various techniques must appear in the book itself.
Techniques for dealing with missing data preferable, but not necessary...
 A: Although specific to Stata, I've found Scott Long's book, The Workflow of Data Analysis Using Stata, invaluable in the area of data management and preparation. The author gives a lot of helpful advice regarding good practices in data management, such as cleaning and archiving data, checking for outliers and dealing with missing data.
A: For SAS, there is Ron Cody's Data Cleaning Techniques using SAS Software. There is a saying on SAS-L: "You can never go wrong with a book by Ron Cody"
A: If you have the basics (identifying outliers, missing values, weighting, coding) depending on the topic there's a lot more in the plain academic literature to be found. For example in survey research (which is a topic where many things can go wrong, and prone to many sources of bias) there are a lot of good articles to be found. 
When preparing for regular crossectional regression, things may be less complex. 
Problem there may for example that you remove too many 'outliers' and thus artificially fitting your model well. 
I thus also recommend you besides learning good techniques, also keep common sense in mind. Make sure you apply the techniques rightfully and not blindly. As for the software discussion in the other answers. I think SPSS is not bad for data preparation (I also heard good things about SAS) depending on your dataset size. The drop down menus are very intuitive.
But as a direct answer to your question, academic literature may or may not be a very good source for your data preparation depending on the topic and analysis.
