Big data is fuelling the digital revolution in an increasingly knowledge driven and connected
society by offering big data analytics and computational intelligence based solutions to …

Grouping problems are combinatorial optimization problems, most of them NP-hard, related
to the partition of a set of items into different groups or clusters. Given their numerous real …

Big Data has significant impact in developing functional smart cities and supporting modern
societies. In this paper, we investigate the importance of Big Data in modern life and …

Cancer is one of the leading causes of morbidity and mortality worldwide with increasing
prevalence. Breast cancer is the most common type among women, and its early diagnosis …

Grouping problems are a special type of combinatorial optimization problems that have
gained great relevance because of their numerous real-world applications. The solution …

Time series forecasting, such as stock price prediction, is one of the most important
complications in the financial area as data is unsteady and has noisy variables, which are …

This article presents a newly proposed selection process for genetic algorithms on a class of
unconstrained optimization problems. The k-means genetic algorithm selection process …

Data clustering is a well-known data analysis technique for organizing unlabeled datapoints
into clusters on the basis of similarity measures. The real-world applications of data …

Discretization is an important preprocessing technique, especially in classification problems.
It reduces and simplifies data, accelerates the learning process, and improves learner …

RIPPER is certainly one of the best rule induction algorithms. In RIPPER, the order in which
the rules are learned is important because the first rule to be fired determines the class of the …