Andreas L. Prodromidis

Learn More
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(More)
Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach to this objective is to apply various machine learning algorithms to compute descriptive models of the available data. Here, we explore one of the main challenges in this research area, the development of techniques that(More)
We describe initial experiments using meta-learning techniques to learn models of fraudulent credit card transactions. Our experiments reported here are the first step towards a better understanding of the advantages and limitations of current meta-learning strategies on real-world data. We argue that, for the fraud detection domain, fraud catching rate(More)
CREDIT CARD TRANSACTIONS CONtinue to grow in number, taking an ever-larger share of the US payment system and leading to a higher rate of stolen account numbers and subsequent losses by banks. Improved fraud detection thus has become essential to maintain the viability of the US payment system. Banks have used early fraud warning systems for some years.(More)
JAM is a powerful and portable agent-based distributed data mining system that employs metalearning techniques to integrate a number of independent classifiers (models) derived in parallel from independent and (possibly) inherently distributed databases. Although meta-learning promotes scalability and accuracy in a simple and straightforward manner, brute(More)
JAM is a powerful and portable agent-based distributed data mining system that employs meta-learning techniques to integrate a number of independent classifiers (concepts) derived in parallel from independent and (possibly) inherently distributed databases. Although metalearning promotes scalability and accuracy in a simple and straightforward manner, brute(More)
In this paper we describe the results achieved using the JAM distributed data mining system for the real world problem of fraud detection in financial information systems. For this domain we provide clear evidence that state-of-the-art commercial This research is supported in part by grants from DARPA (F30602-96-1-0311) and NSF (IRI96-32225 and(More)
Fraud detection has become an important issue to be explored. Fraud detection involves identifying fraud as quickly as possible once it has been perpetrated. Fraud is often a dynamic and challenging problem in Credit card lending business. Credit card fraud can be broadly classified into behavioral and application fraud, with behavioral fraud being the more(More)
In this paper we present a detailed analysis of dynamic partitioning in different distributedmemory parallel environments based on experimental and analytical methods. We develop an experimental testbed in computing environments based on the IBM SP2 and a network of workstations. We also use a general analytic model of dynamic partitioning. This(More)