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 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.
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.
Here we describe the design of an evolutionary algorithm for the problem of multi-view data clustering. The use of a many-objective evolutionary algorithm addresses limitations of previous work, as the resulting method should be capable of scaling to settings with four or more views.
A clustering solution is represented by a new multi-prototype encoding scheme comprised of three parts: activation thresholds (binary values), cluster centroids (real values), and cluster labels (integer values)
This paper presents an up-to-date review of all major nature-inspired metaheuristic algorithms used thus far for automatic clustering.