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.
This paper proposes an automatic method for TM construction based on learning rules. Our method considers the background knowledge of the RDBs during the building process and was implemented and applied on a representative set of 15 RDBs.