Distributed Data Mining Based on Grid Services Pool

This paper presents a Distributed datamining platform on Grid services pool (DDM-GSP), whichcombines grid services pool with distributed data miningto solve problems of traditional distributed data mining.Meanwhile, this paper implements parallel distributed genetic algorithm to resolve complex function optimizationon basis of DDM-GSP. Simulation experiments show thatfor concentrative mining, with the augmentation of population sizes, the convergent speed of standard genetic algorithm is improved by 39 times and computing time is improved by 81 times. However, improvement of populationdimensions, the average consumptive time of distributedgenetic algorithm on grid is about 31.7% less than standard concentrative genetic algorithm, while about 28.6%by contrast to traditional parallel genetic algorithm.