ABSTRACT
With the advances in mobile and wireless technologies, there has been a rise in applications that track and share the users' geospatial data. People use several social networking sites such as Twitter, Facebook and Flickr, where they share their status updates. With the integration of Global Positioning System (GPS) with mobile phones, it is now possible to share one's locations on these social networks. GPS allows us to record and track a person's movement along with the timestamp. The data set obtained from these GPS logs is vast and is widely used to analyze the users' movement patterns. Specifically, we can find out significant locations based on the number of users present at that location and the time spent by them at such places. Once significant places have been identified, it is also possible to identify the semantic importance of these locations. This paper presents an overview of the clustering techniques used to find important places of interest using large GPS based mobility datasets. Four clustering algorithms, K-Means, DBSCAN, OPTICS and Hierarchical, are implemented, and performance is tested using real-time data of 50 users collected over 2--5 years. Performance summary depicts that K-Means and DBSCAN perform well for spatial data.
- Mingqi Lv, Ling Chen, Zhenxing Xu, Yinglong Li, Gencai Chen, "The discovery of personally semantic places based on trajectory data mining", Neurocomputing, 2015 https://doi.org/10.1016/j.neucom.2015.08.071Google ScholarDigital Library
- M. Ester, H.-P. Kriegel, J. Sander and X. Xu, "A Density Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise", Proc. Second International Conference on Knowledge Discover and Data Mining (KDD), pp. 226--231, 1996.Google ScholarDigital Library
- M. Ankerst, M. Breunig, H-P. Kreigel and J. Sander, "OPTICS: Ordering Poits to Identify the Clustering Structure", Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 49--60, 1999.Google ScholarDigital Library
- Zhou, C., Frankowski, D., Ludford, P., Shekhar, S. and Terveen, L., "Discovering personal gazetteers: an interactive clustering approach", Proceedings of GIS, pp. 266--273. ACM, New York, 2004Google Scholar
- A.T. Palma, V. Bogorny, B. Kuijpers and L.O. Alvares, "A clustering-based approach for discovering interesting places in trajectories", Proceedings of SAC, 2008Google ScholarDigital Library
- Steven Van Canneyt, Steven Schockaert, Olivier Van Laere and Bart Dhoedt "Detecting Places Of Interest using Social Media", 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2012Google Scholar
- Enrico Steiger, René Westerholt, Bernd Resch and Alexander Zipf, "Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data", Computers, Environment and Urban Systems, Volume 54, November 2015Google Scholar
- Gennady Andrienko, Natalia Andrienko, Christophe Hurter, Salvatore Rinzivillo and Stefan Wrobel, "Scalable Analysis of Movement Data for Extracting and Exploring Significant Places", IEEE Transactions on Visualization and Computer Graphics, Vol. 19, No. 7, July 2013Google ScholarDigital Library
- Md Reaz Uddin, Chinya Ravishankar and Vassilis J. Tsotras, "Finding Regions of Interest from Trajectory Data", 12th International Conference on Mobile Data Management, 2011Google Scholar
- Tanusri Bhattacharya, Lars Kulik, James Bailey, "Automatically recognizing places of interest from unreliable GPS data using spatio-temporal density estimation and line intersections", Pervasive and Mobile Computing 19, 2015Google ScholarDigital Library
- Miao Lin and Wen-Jing Hsu, "Mining GPS data for mobility patterns: A survey", Pervasive and Mobile Computing 12, 2014Google ScholarCross Ref
- Xin Cao, Gao Cong, Christian and S. Jensen, "Mining Significant Semantic Locations from GPS Data", Proceedings of the VLDB Endowment, Vol. 3, No. 1, 2010Google Scholar
- Peter J. Rousseeuw, "Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis", Computational and Applied Mathematics. 20: 53--65, 1987 https://doi.org/10.1016/0377-0427(87)90125-7Google ScholarDigital Library
- Chen Guang-xue, Li Xiao-zhou, Chen Qi-feng and Li Xiaozhou, "Clustering Algorithms for Area Geographical Entities in Spatial Data Mining", Seventh International Conference on Fuzzy Systems and Knowledge Discovery, pp. 1630--1633, 2010Google ScholarCross Ref
- B. Rama, Jayashree P., Salim Jiwani, "A Survey on Clustering Current Status and Challenging Issues", (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 9, pp. 2976--2980, 2010Google Scholar
- Amit Saxena, Mukesh Prasad, Akshansh Gupta, Neha Bharill, Om Prakash Patel, Aruna Tiwari, Mengjoo Er, Weiping Ding, Chin-Teng Lin, "A Review of Clustering Techniques and Developments", Neurocomputing, 2017, https://doi.org/10.1016/j.neucom.2017.06.053.Google ScholarDigital Library
Index Terms
- Clustering Algorithms for Spatial Data Mining
Recommendations
Clustering spatial data with a geographic constraint: exploring local search
Spatial data objects that possess attributes in the optimization domain and the geographic domain are now widely available. For example, sensor data are one kind of spatial data objects. The location of a sensor is an attribute in the geographic domain, ...
Mining lidar data with spatial clustering algorithms
Clustering algorithms have been an important area of research in the domain of computer science for data mining of patterns in various kinds of data. This process can identify major patterns or trends without any supervisory information such as data ...
A density-based spatial clustering algorithm considering both spatial proximity and attribute similarity
Geometrical properties and attributes are two important characteristics of a spatial object. In previous spatial clustering studies, these two characteristics were often neglected. This paper addresses the problem of how to accommodate geometrical ...
Comments