Please Scroll down for Article International Journal of Geographical Information Science Windowed Nearest Neighbour Method for Mining Spatio-temporal Clusters in the Presence of Noise Windowed Nearest Neighbour Method for Mining Spatio-temporal Clusters in the Presence of Noise
Please Scroll down for Article International Journal of Geographical Information Science Windowed Nearest Neighbour Method for Mining Spatio-temporal Clusters in the Presence of Noise Windowed Nearest Neighbour Method for Mining Spatio-temporal Clusters in the Presence of Noise
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通讯作者：
T. Pei;Chenghu Zhou;A. Zhu;Baolin Li;C. Qin
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作者：
T. Pei;Chenghu Zhou;A. Zhu;Baolin Li;C. Qin
本文可用于研究、教学和私人学习目的。明确禁止以任何形式向任何人进行任何实质性或系统性复制、再分发、转售、出借或再许可、系统性供应或分发。出版商不提供任何明示或暗示的保证，也不代表内容将是完整的、准确的或最新的。任何说明、配方和药物剂量的准确性均应通过主要来源进行独立验证。对于因使用本材料而直接或间接引起的任何损失、诉讼、索赔、程序、要求或费用或损害，出版商不承担任何责任。在时空数据集中，由于时间和空间的耦合以及噪声的干扰，识别时空簇是很困难的。先前的方法采用窗口扫描技术或时空距离技术来识别时空簇。虽然很容易实现，但它们在选择分类参数时存在主观性。在本文中，我们使用窗口第 k 个最近 (WKN) 距离（事件与其时间距离不大于指定时间窗口宽度的一半 [TWW] 的事件中的第 k 个地理最近邻之间的地理距离）来区分时空数据中的聚类和噪声。加窗最近邻（WNN）方法由四个步骤组成。第一个是构建一系列 TWW 因子，利用该序列可以计算不同时间尺度下事件的 WKN 距离。其次，TWW的适当值（即适当的时间尺度，在对事件进行分类时，误报的数量可能达到最低值）由所识别的聚类事件的密度的局部最大值指示，该局部最大值是通过使用期望最大化算法在不同的TWW上计算的。第三，然后用确定的TWW导出用于分类的WKN距离的阈值。在第四步中，在确定的 TWW 处识别的聚类事件根据其在地理-时间空间中的密度连通性连接成聚类。模拟数据和地震案例研究的结果表明，WNN 方法可以有效地识别时空簇。 WNN的新颖之处在于它不仅可以识别任意形状和不同时空密度的时空簇，而且可以显着降低分类过程中的主观性。 ……
This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. In a spatio-temporal data set, identifying spatio-temporal clusters is difficult because of the coupling of time and space and the interference of noise. Previous methods employ either the window scanning technique or the spatio-temporal distance technique to identify spatio-temporal clusters. Although easily implemented, they suffer from the subjectivity in the choice of parameters for classification. In this article, we use the windowed kth nearest (WKN) distance (the geographic distance between an event and its kth geographical nearest neighbour among those events from which to the event the temporal distances are no larger than the half of a specified time window width [TWW]) to differentiate clusters from noise in spatio-temporal data. The windowed nearest neighbour (WNN) method is composed of four steps. The first is to construct a sequence of TWW factors, with which the WKN distances of events can be computed at different temporal scales. Second, the appropriate values of TWW (i.e. the appropriate temporal scales, at which the number of false positives may reach the lowest value when classifying the events) are indicated by the local maximum values of densities of identified clustered events, which are calculated over varying TWW by using the expectation-maximization algorithm. Third, the thresholds of the WKN distance for classification are then derived with the determined TWW. In the fourth step, clustered events identified at the determined TWW are connected into clusters according to their density connectivity in geographic–temporal space. Results of simulated data and a seismic case study showed that the WNN method is efficient in identifying spatio-temporal clusters. The novelty of WNN is that it can not only identify spatio-temporal clusters with arbitrary shapes and different spatio-temporal densities but also significantly reduce the subjectivity in the classification process. …