The temporal and spatial characteristics of users are involved in most Internet of Things (IoT) applications. The spatial and temporal movement patterns of users are the most direct manifestation of the temporal and spatial characteristics. The user’s interests, activities, experience and other characteristics are reflected by mobile mode. In view of the low clustering efficiency of moving objects in convergent pattern mining in the IoT, a spatiotemporal feature mining algorithm based on multiple minimum supports of pattern growth is proposed. Based on the temporal characteristics of user trajectories, frequent and asynchronous periodic spatiotemporal movement patterns are mined. Firstly, the location sequence is modeled, and the time information is added to the model. Then, a mining algorithm of asynchronous periodic sequential pattern is adopted. The algorithm is based on multiple minimum supports of pattern growth. According to multiple minimum supports, the sequential pattern of asynchronous period is mined deeply and recursively. Finally, the proposed method is validated and evaluated by Gowalla dataset, in which the user characteristics are truly reflected. It is shown by the experimental results that the average pointwise mutual information (PWI) of the proposed algorithm reaches 0.93. And the algorithm is proved to be effective and accurate.

中文翻译：

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物联网中基于模式增长多重最小支持度的时空特征挖掘算法

大多数物联网（IoT）应用都涉及用户的时空特性。用户的时空运动模式是时空特征的最直接体现。用户的兴趣、活动、体验等特征通过移动模式来体现。针对物联网收敛模式挖掘中移动对象聚类效率低的问题，提出了一种基于模式增长多个最小支持度的时空特征挖掘算法。基于用户轨迹的时间特征，挖掘频繁和异步的周期性时空运动模式。首先对位置序列进行建模，并在模型中加入时间信息。然后，采用异步周期序列模式挖掘算法。该算法基于模式增长的多个最小支持。根据多个最小支持度，对异步周期序列模式进行深度递归挖掘。最后，通过Gowalla数据集对所提出的方法进行验证和评估，真实反映了用户特征。实验结果表明，该算法的平均逐点互信息（PWI）达到0.93。并且该算法被证明是有效和准确的。实验结果表明，该算法的平均逐点互信息（PWI）达到0.93。并且该算法被证明是有效和准确的。实验结果表明，该算法的平均逐点互信息（PWI）达到0.93。并且该算法被证明是有效和准确的。