Pub Date : 2017-01-01Epub Date: 2017-01-05DOI: 10.1007/978-3-319-22786-3_32
Xuan Shi
Introduced in 2007, affinity propagation (AP) is a relatively new machine learning algorithm for unsupervised classification that has seldom been applied in geospatial applications. One bottleneck is that AP could hardly handle large data, and a serial computer program would take a long time to complete an AP calculation. New multicore and manycore computer architectures, combined with application accelerators, show promise for achieving scalable geocomputation by exploiting task and data levels of parallelism. This chapter introduces our recent progress in parallelizing the AP algorithm on a graphics processing unit (GPU) for spatial cluster analysis, the potential of the proposed solution to process big geospatial data, and its broader impact for the GIScience community.
亲和传播（AP）于 2007 年推出，是一种相对较新的无监督分类机器学习算法，但很少应用于地理空间应用。该算法的一个瓶颈是难以处理大量数据，而且串行计算机程序需要很长时间才能完成亲和传播计算。新的多核和多核计算机体系结构与应用加速器相结合，通过利用任务和数据层面的并行性，有望实现可扩展的地理计算。本章将介绍我们最近在图形处理器（GPU）上并行化 AP 算法以进行空间聚类分析方面取得的进展、所提出的解决方案在处理大型地理空间数据方面的潜力及其对地理信息系统科学界的广泛影响。
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