High performance clustering for large data warehouses using peer-to-peer genetic algorithm

Abstract

High volumes of data pose a challenge to the scalability of data mining algorithms. Dividing this data into equal partitions and processing it in parallel naturally becomes a choice. Peer-to-peer computing exposes a bright source for exploiting parallelism and maintaining scale-up capability. We consider parallelism in genetic algorithms while computing the fitness of the population individuals (chromosomes). This strategy has an edge over its counterpart, that is, parallelism in genetic operators, because genetic operators tend to be computationally cheap. Simply speaking this scheme supports large data sets, that is. larger the data size, larger will be the degree of parallelism achieved.