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 …

During decision making, we are often confronted by a huge amount of factors. These factors
may be either an advantage or a disadvantage to a decision objective. For the purpose of …

An ensemble is a group of learning models that jointly solve a problem. However, the
ensembles generated by existing techniques are sometimes unnecessarily large, which can …

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 …

One of the defining challenges for the KDD research community is to enable inductive
learning algorithms to mine very large databases. This paper summarizes, categorizes, and …

Many large organizations have multiple data sources, such as different branches of an
interstate company. While putting all data together from different sources might amass a …

This paper presents new measures, based on the induced decision tree, to characterise
datasets for meta-learning in order to select appropriate learning algorithms. The main idea …

Many large organizations have multiple databases distributed in different branches, and
therefore multi-database mining is an important task for data mining. To reduce the search …

Multi-database mining is an important research area because (1) there is an urgent need for
analyzing data in different sources,(2) there are essential differences between mono-and …

In this paper we study methods that combine multiple classification models learned over
separate data sets. Numerous studies posit that such approaches provide the means to …