Spatiotemporal feature mining algorithm based on multiple minimum supports of pattern growth in Internet of Things
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Spatiotemporal feature mining algorithm based on multiple minimum supports of pattern growth in Internet of Things Anqing Zhu1,2
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. Keywords Spatiotemporal feature mining · Multiple minimum supports · Internet of Things (IoT) · Asynchronous period · Time-series feature
* Anqing Zhu [email protected] 1
Management School, Jinan University, Guangzhou 510000, Guangdong, China
2
Guangzhou Sunrise Technology Co., Ltd., Guangzhou 510000, Guangdong, China
13
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A. Zhu
1 Introduction In the era of big data, there is an urgent need for new tools and technical means to deal with large spatial data, and to transform data into knowledge efficiently and intelligently. Thus, the so-called data-intensive, knowledge-poor scientific problems can be solved. In this context, data mining technology emerges and shows a positive trend of development. Data mining technology is a new crosscutting technology. And it is the process of obtaining implicit, unknown and potentially useful information from a large number of data [1–3]. Integrating database technology, pattern recognition, artificial intelligence, statistics, machine learning, expert learning and other technologies, data mining has been widely used in various fields. With the development of network technology and information technology, the Internet of Things (IoT) is widely concerned by academia and industry. The IoT has become a link between the physical world and the virtual world and gradually depicts the vision of the interconnection of all things. According to the latest report released by Research Institute Gartner, the number
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