A polar projection-based algorithm is proposed to reduce the computational complexity associated with dimension reduction in unsupervised learning. This algorithm employs $K$ -means clustering. A new distance metric is developed to account for peak consumption in cluster consumer load profiles. It is used to cluster the load profiles according to both total and peak consumption. To accelerate the clustering process, a stochastic-based approach is developed to reduce the search space to find the cluster centers. Numerical results are presented which show a significant reduction in computational complexity using both polar-based and stochastic-based clustering compared to conventional approaches. Further, the estimation error is low.

中文翻译：

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智能电表数据的快速大数据分析

提出了一种基于极点投影的算法，以减少与无监督学习中的降维相关的计算复杂性。该算法采用 $ K $ -表示聚类。开发了一种新的距离度量标准，以解决集群用户负载配置文件中的峰值消耗。它用于根据总消耗量和峰值消耗对负载曲线进行聚类。为了加快聚类过程，开发了一种基于随机的方法来减少寻找聚类中心的搜索空间。数值结果表明，与传统方法相比，使用基于极性的聚类和基于随机的聚类都显着降低了计算复杂性。此外，估计误差低。