Application of big data optimal selection algorithm in project bidding scheme selection

Abstract

In order to solve the problems of low efficiency and poor accuracy of traditional bidding scheme selection, an application of big data optimal selection algorithm in project bidding scheme selection is put forward. In this paper, the whole bidding scheme is processed by big data sampling method to speed up the big data processing process. By clustering the sampling results with Single method, the location of the centroid of the natural cluster of big data is determined. Then, the position of the centroid of the natural cluster of big data is adjusted by using the mean updating method to determine its accurate position. On this basis, the bidding scheme set is divided and clustered to realize the classification of bidding schemes. In order to choose the best bidding scheme, we build a projection pursuit model. Through this model, the optimal bidding scheme can be found. The experimental results show that the clustering error of the proposed algorithm is less than 6%, the accuracy is over 94%, and it has obvious advantages in computing speed.