JAM: java agents for meta-learning over distributed databases

Abstract

In this paper, we describe the JAM system, a dis-tributed, 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 mul-tiple models that were learned (perhaps) at dif-ferent sites. It employs a special distribution mechanism which allows the migration of the de-rived models or classifier agents to other remote sites. We describe the overall architecture of the JAM system and the specific implementation cur-rently under development at Columbia Univer-sity. 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