Hi all
I'm interested in the problem of patterning mining among players of social networking games. For example detecting cheaters of a game, given a company's user database. So far I have been following the usual recipe for a data mining project:
construct a data warehouse that aggregates significant information
select a classifier, and train it with a subsectio of records from the warehouse
validate classifier with another test set
lather, rinse, repeat
Surprisingly, I've found very little in this area regarding literature, best practices, etc. I am hoping to crowdsource the information gathering problem here. Specifically what I'm looking for:
What classifiers have worked will for this type of pattern mining (it seems highly temporal, users playing games, users receiving rewards, users transferring prizes etc).
Are there any highly agreed upon attributes specific to social networking / gaming data?
What is a practical amount of information that should be considered? One problem I've run into is data overload, where queries and data cleansing may take days to complete.
Related to point above, what hardware resources are required to produce results? I've found it difficult to estimate the amount of computing power I will require for production use. It has become apparent that a white box in the corner does not have enough horse-power for such a project. Are companies generally resorting to cloud solutions? Are they buying clusters?
Basically, any resources (theoretical, academic, or practical) about implementing a social networking / gaming pattern-mining program would be very much appreciated.
Thanks.
