Abstract Top Down Induction of Decision Trees (TDIDT) is the most commonly used method
of constructing a model from a dataset in the form of classification rules to classify previously …

Inducing rules from very large datasets is one of the most challenging areas in data mining.
Several approaches exist to scaling up classification rule induction to large datasets, namely …

In a world where data is captured on a large scale the major challenge for data mining
algorithms is to be able to scale up to large datasets. There are two main approaches to …

In a world where massive amounts of data are recorded on a large scale we need data
mining technologies to gain knowledge from the data in a reasonable time. The Top Down …

Induction of classification rules is one of the most important technologies in data mining.
Most of the work in this field has concentrated on the Top Down Induction of Decision Trees …

One of the key technologies of data mining is the automatic induction of rules from
examples, particularly the induction of classification rules. Most work in this field has …

Abstract Machine learning has been studied intensively during the past two decades. One
motivation has been the desire to automate the process of knowledge acquisition during the …

Rule learning is one of the most popular areas in machine learning research, because the
outcome of learning is to produce a set of rules, which not only provides accurate predictions …

One of the known classification approaches in data mining is rule induction (RI). RI
algorithms such as PRISM usually produce If-Then classifiers, which have a comparable …

In order to gain knowledge from large databases, scalable data mining technologies are
needed. Data are captured on a large scale and thus databases are increasing at a fast …