Spatiotemporal feature mining algorithm based on multiple minimum supports of pattern growth in Internet of Things

• Published in: The Journal of supercomputing Vol. 76; no. 12; pp. 9755 - 9771
• Main Author: Zhu, Anqing
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2020; Springer Nature B.V
• Subjects: Algorithms; Clustering; Compilers; Computer Science; Data mining; Intelligent and Pervasive Computing for Cyber-Physical Systems; Internet of Things; Interpreters; Pattern analysis; Processor Architectures; Programming Languages; Multiple minimum supports; Internet of Things (IoT); Asynchronous period; Spatiotemporal feature mining; Time-series feature
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-020-03217-x

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.