| Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. One means of acquiring knowledge from databases is to apply various machine learning algorithms that compute descriptive representations of the data as well as patterns that may be exhibited in the data.; Most of the current generation of learning algorithms, however, are computationally complex and require all data to be resident in main memory which is clearly untenable for many realistic problems and databases. In this dissertation we investigate data mining techniques that scale up to large and physically distributed data sets. Specifically, we describe the JAM system (Java Agents for Meta-learning), an extensible agent-based distributed data mining system that supports the remote dispatch and exchange of agents among participating data sites and employs meta-learning techniques to combine the multiple models that are learned. Several important desiderata of data mining systems are addressed (i.e., scalability, efficiency, portability, compatibility, adaptivity, extensibility and effectiveness) and a combination of AI-based methods and distributed systems techniques are presented.; We applied JAM on the real-world data mining task of modeling and detecting credit card fraud with notable success. Inductive learning agents are used to compute detectors of anomalous or errant behavior over inherently distributed data sets and meta-learning methods integrate their collective knowledge into higher level classification models or meta-classifiers. By supporting the exchange of models or classifier agents among data sites, our approach facilitates the cooperation between financial organizations and provides unified and cross-institution protection mechanisms against fraudulent transactions. |
