clustering

An AI-based system named C3-IoC to help students explore career paths in IT according to their level of education, skills and prior experience.

An evolutionary method to multi-view clustering, the algorithm optimizes multiple objectives simultaneously to effectively explore the space of candidate trade-offs between the data views.

This article explores the interaction between dissimilarity measures and internal cluster validity techniques in the context of multi-objective clustering.

This exploratory work aims to avoid this premature integration of attribute types prior to cluster analysis through a multi-objective evolutionary algorithm called MVMC.

A visualizer of SSc biclusters.

This work evaluates the effectiveness of 22 different CVIs used as fitness functions in an evolutionary clustering algorithm.

We present an evolutionary algorithm for the problem of multiview cluster analysis by exploiting recent advances in the field of evolutionary optimization.

We investigate the interaction between the clustering criteria employed in a multi-objective algorithm and the distance functions on which these criteria operate.

A CVI toolbox for estimating the number of clusters.

This paper presents a systematic taxonomical overview and bibliometric analysis of the trends and progress in nature-inspired metaheuristic clustering approaches from the early attempts in the 1990s until today’s novel solutions.