Abstract: With the population of the parallel framework of MapReduce,the parallelization of various types of Data Mi-ning algorithms is becoming a hot area of research.Principle components analysis(PCA) algorithm is getting more and more attention too.Summarizing the recent research result of the parallelization of PCA,we found that these PCA algorithms are not fully parallelized,especially the process of calculating the eigenvalue of the matrix.Whole process of PCA algorithm is divided into two stages,which are solution of the correlation coefficient matrix and the singular value decomposition of the matrix.Through the combination of the most popular MapReduce parallel framework and the QR decomposition of matrix,a new way to parallel the SVD was proposed in this paper.Analyzing the calculation speed of the parallel algorithm through the experiment on the data set which is consisted of random produced double floating point matrix of different dimensions,the result with the traditional serial algorithm was compared to show the efficiency improvement of the mentioned algorithm.Then we integrated the SVD algorithm into the PCA algorithm,and proposed the parallel computing process of the correlation coefficient matrix,and this will parallel the two stages of PCA algorithm.Subsequently,we conducted the comparison between the existing not fully parallelized PCA algorithm and normal PCA algorithm with the proposed algorithm on different dimensions of the matrix.Then analying the speed-up ratio of the proposed algorithm,we can find that our algorithm will consume less time when processing massive data set.

Key words: Principle components analysis,SVD,MapReduce