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An Improved Clustering Approach for Identifying Significant Locations from Spatio-temporal Data

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Abstract

The rapid development and adoption of Internet-of-Things (IoT) and sensors such as Global Positioning System (GPS) in our daily life allows gathering of wealth of information. This tremendous amount of sensor data can be clustered to infer information about the whereabouts of users. Researchers have proposed various algorithms for analyzing spatio-temporal data and clustering such datasets. However, a big challenge is to discover clusters with large density variation and to solve this problem most of the existing clustering algorithms manually set the input parameters. In the Density-based spatial clustering (DBSCAN) clustering algorithm, the epsilon and minimum point parameters have to be set manually for further computation. In this paper, we propose an improved DBSCAN algorithm to adaptively set minimum point parameter for dynamic length of data, based on merge sort and silhouette analysis. Validation of the proposed algorithm has been done on GeoLife GPS dataset. Experimental results show that the proposed algorithm is competitive with state-of-the-art methods for identifying users’ significant locations and whereabouts with the density variation. The paper also aims to highlight the issues pertaining to the invasion of user privacy by the potential application of the clustering algorithm on the aforementioned data.

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Acknowledgements

The authors are grateful to the Ministry of Human Resource Development (MHRD) of Government of India for supporting this research under Design Innovation Center (MHRD-DIC) under the subtheme “TRAFFIC SENSING AND IT”. The authors also would like to thank Akanksha Chuchra, (akanksha.chuchra96@gmail.com), and Dravita Singla (dravitasingla1995@gmail.com); Intern, DIC, UIET, for their contribution to the project.

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  1. Department of Computer Science and Engineering, UIET Panjab University, Chandigarh, India

    Rigzin Angmo & Naveen Aggarwal

  2. Department of Information Technology, UIET Panjab University, Chandigarh, India

    Veenu Mangat

  3. Deloitte USI, Deloitte, Hyderabad, India

    Anurag Lal

  4. Infosys, Pune, India

    Simarpreet Kaur

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  1. Rigzin Angmo
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Correspondence to Rigzin Angmo.

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Angmo, R., Aggarwal, N., Mangat, V. et al. An Improved Clustering Approach for Identifying Significant Locations from Spatio-temporal Data. Wireless Pers Commun 121, 985–1009 (2021). https://doi.org/10.1007/s11277-021-08668-w

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