Clustering is perhaps one of the most popular approaches used in unsupervised machine learning. There’s a huge number of different methods and algorithms that have been designed in the last decades related to this “blind pattern search”, some of these approaches are based on bio-inspired methods such as Evolutionary Computation, Swarm Intelligence or Neural Networks among others. In the last years, and due to the fast growing of Big Data problems, some interesting advances and new approaches are currently being developed in this area, new algorithms like online clustering and streaming clustering are appearing.
These new algorithms try to solve classical problems in Clustering and deal with the new features of these new kind of problems. This keynote lecture will provide some basics on both, Clustering methods and bio-inspired computation, and how they have been combined to improve the quality of these algorithms, to later show the main features that Big Data needs to obtain reliable clustering approaches. Finally, some practical examples and applications will be described to show how these new algorithms are evolving to be used in the near future in complex and dynamic environments.