Abstract
In order to solve the problem of poor clustering effect of K-means algorithm when dealing with massive high-dimensional data on Hadoop platform, and the existing improved algorithm is not conducive to parallelization. An improved K-means algorithm based on Hash is proposed on Hadoop platform. Firstly, the massive high-dimensional data is mapped to a compressed identification space, and then the clustering relationship is mined, and the initial clustering center is selected to avoid the sensitivity of the traditional K-means algorithm to randomly select the initial clustering center, and reduced the number of iterations of the K-means algorithm. Secondly, the overall parallelization of the algorithm is implemented in the framework of Map Reduce, and the degree of parallelization and execution efficiency is enhanced through the mechanisms of partition and combine. Finally, the experiments show that the algorithm not only improves the accuracy and stability of clustering, but also has a good processing speed.
Published Version
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