Discretization is an essential preprocessing technique used in many knowledge discovery
and data mining tasks. Its main goal is to transform a set of continuous attributes into discrete …

This chapter emphasizes on the role played by rough set theory (RST) within the broad field
of Machine Learning (ML). As a sound data analysis and knowledge discovery paradigm …

The extraction of knowledge from a huge volume of data using rough set methods requires
the transformation of continuous value attributes to discrete intervals. This paper presents a …

Discretization of continuous attributes is an important task in rough sets and many
discretization algorithms have been proposed. However, most of the current discretization …

Crowdfunding is the practice of funding a project or venture by raising monetary
contributions from a large number of people, typically via the Internet. Lendwithcare is …

Discretization plays a major role as a data preprocessing technique used in machine
learning and data mining. Recent studies have focused on multivariate discretization that …

Discretization is an essential preprocessing technique used in many knowledge discovery
and data mining tasks. Its main goal is to transform a set of continuous attributes into discrete …

Association rule has shown its usefulness in the gene expression data based disease
diagnosis for its good interpretability. The large number of rules generated from the high …

Most discretization algorithms focus on the univariate case. In general, they take into
account the target class or interval-wise frequency of data. In so doing, useful information …

It is being widely studied how to extract knowledge from a decision table based on rough set
theory. The novel problem is how to discretize a decision table having continuous attribute …