Guest Editorial: Thematic Issue on ‘Metaheuristics for large scale data mining’

Abstract

Data mining and knowledge discovery are crucial techniques acrossmany scientific disciplines. Recent developments such as the Genome Project (and its successors) or the construction of the Large Hadron Collider have provided the scientific community with vast amounts of data. Metaheuristics and other evolutionary algorithms have been successfully applied to a large variety of data mining tasks. Competitive metaheuristic approaches are able to deal with rule, tree and prototype induction, neural networks synthesis, fuzzy logic learning, and kernel machines, to mention but a few. Moreover, the inherent parallel nature of somemetaheuristics (e.g. evolutionary approaches, particle swarms, ant colonies, etc.) makes them perfect candidates for approaching very largescale data mining problems. Although a number of recent techniques have applied these methods to complex data mining domains, we are still far from having a deep and principled understanding of how to scale them to datasets of terascale, petascale or even larger scale. In order to achieve and maintain a relevant role in large scale data mining, metaheuristics need, among other features, to have the capacity of processing vast amounts of data in a reasonable time frame, to use efficiently the unprecedented computer power available nowadays due to advances