The term 'Big Data'has spread rapidly in the framework of Data Mining and Business
Intelligence. This new scenario can be defined by means of those problems that cannot be …

Abstract The k-Nearest Neighbors classifier is a simple yet effective widely renowned
method in data mining. The actual application of this model in the big data domain is not …

In the era of big data, analyzing and extracting knowledge from large-scale data sets is a
very interesting and challenging task. The application of standard data mining tools in such …

Nowadays, many disciplines have to deal with big datasets that additionally involve a high
number of features. Feature selection methods aim at eliminating noisy, redundant, or …

A Web repository is a large special-purpose collection of Web pages and associated
indexes. Many useful queries and computations over such repositories involve traversal and …

Attribute reduction is one of the important research issues in rough set theory. Most existing
attribute reduction algorithms are now faced with two challenging problems. On one hand …

The k-Nearest Neighbor classifier is one of the most well known methods in data mining
because of its effectiveness and simplicity. Due to its way of working, the application of this …

Inductive Logic Programming (ILP) is a classic machine learning technique that learns first-
order rules from relational-structured data. However, to-date most ILP systems can only be …

Attribute reduction is the key technique for knowledge acquisition in rough set theory.
However, it is still a challenging task to perform attribute reduction on massive data. During …

Attribute reduction is an important application of rough sets. Efficiently reducing massive
dynamic data sets quickly has always been a major goal of researchers. Traditional …