Salvatore Stolfo, Andreas L. Prodromidis, Shelley Tselepis, Wenke Lee, David W. Fan, and Philip K. Chan
In this paper, we describe the JAM system, a distributed, scalable and portable agent-based data mining system that employs a general approach to scaling data mining applications that we call meta-learning. JAM provides a set of learning programs, implemented either as JAVA applets or applications, that compute models over data stored locally at a site. JAM also provides a set of meta-learning agents for combining multiple models that were learned (perhaps) at different sites. It employs a special distribution mechanism which allows the migration of the derived models or classifier agents to other remote sites. We describe the overall architecture of the JAM system and the specific implementation currently under development at Columbia University. One of JAM’s target applications is fraud and intrusion detection in financial information systems. A brief description of this learning task and JAM’s applicability are also described. Interested users may download JAM from http://www.cs.columbia.edu/~sal/JAM/PROJECT.