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A K-Main Routes Approach to Spatial Network Activity Summarization

机译：空间网络活动总结的K-主路线方法

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摘要

Data summarization is an important concept in data mining for finding a compact representation of a dataset. In spatial network activity summarization (SNAS), we are given a spatial network and a collection of activities (e.g., pedestrian fatality reports, crime reports) and the goal is to find (k) shortest paths that summarize the activities. SNAS is important for applications where observations occur along linear paths such as roadways, train tracks, etc. SNAS is computationally challenging because of the large number of (k) subsets of shortest paths in a spatial network. Previous work has focused on either geometry or subgraph-based approaches (e.g., only one path), and cannot summarize activities using multiple paths. This paper proposes a K-Main Routes (KMR) approach that discovers (k) shortest paths to summarize activities. KMR generalizes K-means for network space but uses shortest paths instead of ellipses to summarize activities. To improve performance, KMR uses network Voronoi, divide and conquer, and pruning strategies. We present a case study comparing KMR's network-based output (i.e., shortest paths) to geometry-based outputs (e.g., ellipses) on pedestrian fatality data. Experimental results on synthetic and real data show that KMR with our performance-tuning decisions yields substantial computational savings without reducing summary path coverage.
机译：数据汇总是数据挖掘中用于查找数据集的紧凑表示形式的重要概念。在空间网络活动总结（SNAS）中，我们获得了一个空间网络和一系列活动（例如行人死亡报告，犯罪报告），目标是找到（k）总结活动的最短路径。对于沿直线路径（例如道路，火车轨道等）进行观测的应用，SNAS非常重要。由于空间网络中最短路径的（k）个子集数量众多，因此SNAS在计算上具有挑战性。先前的工作集中于基于几何或基于子图的方法（例如，仅一条路径），并且无法使用多条路径来总结活动。本文提出了一种K-主要路线（KMR）方法，该方法可以发现（k）条最短路径来总结活动。 KMR概括了网络空间的K-means，但使用最短路径而不是椭圆来总结活动。为了提高性能，KMR使用了Voronoi网络，分而治之和修剪策略。我们提供了一个案例研究，将KMR的基于网络的输出（即最短路径）与行人死亡数据上基于几何的输出（例如椭圆）进行了比较。综合和真实数据的实验结果表明，采用我们的性能调整决策的KMR可以节省大量计算量，而不会减少摘要路径的覆盖范围。

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