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A review of moving object trajectory clustering algorithms

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Abstract

Clustering is an efficient way to group data into different classes on basis of the internal and previously unknown schemes inherent of the data. With the development of the location based positioning devices, more and more moving objects are traced and their trajectories are recorded. Therefore, moving object trajectory clustering undoubtedly becomes the focus of the study in moving object data mining. To provide an overview, we survey and summarize the development and trend of moving object clustering and analyze typical moving object clustering algorithms presented in recent years. In this paper, we firstly summarize the strategies and implement processes of classical moving object clustering algorithms. Secondly, the measures which can determine the similarity/dissimilarity between two trajectories are discussed. Thirdly, the validation criteria are analyzed for evaluating the performance and efficiency of clustering algorithms. Finally, some application scenarios are point out for the potential application in future. It is hope that this research will serve as the steppingstone for those interested in advancing moving object mining.

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References

  • Alvares LO, Bogorny V, Kuijpers B, Macedo JAF, Moelans B, Vaisman A (2007) A model for enriching trajectories with semantic geographical information. In: Proceedings of the 15th annual ACM international symposium on advances in geographic information systems, New York, NY, USA, pp 162–169

  • Alvares LO, Bogorny V, Macedo JF, Moelans B, Spaccapietra S (2007b) Dynamic modeling of trajectory patterns using data mining and reverse engineering. In: Proceedings of the 26th international conference on conceptual modeling, pp 149–154

  • Amorim RC, Mirkin B (2012) Minkowski metric, feature weighting and anomalous cluster initializing in K-means clustering. Pattern Recognit 45(3):1061–1075

    Article  Google Scholar 

  • Ankerst M, Breunig MM, Kriegel HP, Sander J (1999) OPTICS: ordering points to identify the clustering structure. In: The 1999 ACM SIGMOD international conference on management of data, pp 49–60

  • Apeltauer J, Babinec A, Herman D, Apeltauer T (2015) Automatic vehicle trajectory extraction for traffic analysis from aerial video data. Int Arch Photogramm Remote Sens Spat Inf Sci 43(W2):9–15

    Article  Google Scholar 

  • Bashir FI, Khokhar AA, Schonfeld D (2003) Segmented trajectory based indexing and retrieval of video data. In: Proceedings of the 2003 international conference on image processing, vol 2, pp 623–626

  • Beckmann N, Kriegel HP, Schneider R, Seeger B (1990) The R*-tree: an efficient and robust access method for points and rectangles. In: Proceedings Of the SIGMOD’90, ACM, New York, pp 322–331

  • Birant D, Kut A (2007) St-dbscan: an algorithm for clustering spatial and temporal data. Data Knowl Eng 60(1):208–221

    Article  Google Scholar 

  • Boukhers Z, Shirahama K, Li F, Grzegorzek M (2015) Object detection and depth estimation for 3D trajectory extraction. In: Proceedings of the 13th international workshop on content-based multimedia indexing, pp 1–6

  • Buchin K, Buchin M, Gudmundsson J (2010) Constrained free space diagrams: a tool for trajectory analysis. Int J Geogr Inf Sci 24(7):1101–1125

    Article  Google Scholar 

  • Buchin M, Drieme A, Kreveld MV, Sacrist’an V (2011) Segmenting trajectories: a framework and algorithms using spatiotemporal criteria. J Spat Inf Sci 3:33–63

    Google Scholar 

  • Chen JY, Huo QY, Chen P, Xu XZ (2012) Sketch-based uncertain trajectories clustering. In: Proceedings of the 9th international conference on fuzzy systems and knowledge discovery, pp 747–751

  • Chen JD, Lai CF, Meng XF, Xu JL, Hu HB (2007) Clustering moving objects in spatial networks. In: Proceedings of the 12th international conference on database systems for advanced applications, 2007, pp 611–623

  • Chen JY, Wang RD, Liu LX, Song JT (2011) Clustering of trajectories based on Hausdorff distance. In: Proceedings of the 2011 international conference on electronics, communications and control, pp 1940–1944

  • Chen JD, Meng XF, Lai CF (2007) Clustering objects in a road network. J Softw 18:332–344

    Article  Google Scholar 

  • Chen L, Özsu M, Oria V (2005) Robust and fast similarity search for moving object trajectories. In: Proceedings of the 2005 ACM SIGMOD international conference on management of data, ACM, New York, NY, USA, pp 491–502

  • Comer D (1979) The ubiquitous B-tree. Comput Surv 11(2):123–137

    Article  MathSciNet  MATH  Google Scholar 

  • Eiter T, Mannila H (1994) Computing discrete Fréchet distance. Technical report CD-TR 94/64, Technische Universitat Wien

  • Ester M, Kriegel HP, Sander J, Xu X (1996) Density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd international conference on knowledge discovery and data mining, pp 226–231

  • Fileto R, Raffaetà A, Roncato A, Sacenti JAP, May C, Klein D (2014) A semantic model for movement data warehouses. In: Proceedings of the 17th international workshop on data warehousing and OLAP, 2014, pp 47–56

  • Fisher DH (1987) Knowledge acquisition via incremental conceptual clustering. Mach Learn 2(2):139–172

    Google Scholar 

  • Fraley C, Raftery AE (2002) Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc 97(458):611–631

    Article  MathSciNet  MATH  Google Scholar 

  • Gudmundsson J, Kreveld M, Speckmann B (2004) Efficient detection of motion patterns in spatio-temporal data sets. In: Proceedings of the 12th annual ACM international workshop on Geographic information systems, pp 250–257

  • Guha S, Rastogi R, Shim K (2001) CURE: an efficient clustering algorithm for large databases. Inf Syst 26(1):35–58

    Article  MATH  Google Scholar 

  • Guttman A (1984) R-trees: a dynamic index structure for spatial searching. In: Proceedings of the 1984 ACM SIGMOD international conference on management of data, pp 47–57

  • Halkidi M, Batistakis Y, Vazirgiannis M (2001) On clustering validation techniques. J Intell Inf Syst 17(2–3):107–145

    Article  MATH  Google Scholar 

  • Han JW, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann Publishers, San Francisco

    MATH  Google Scholar 

  • Han B, Liu L, Omiecinski E (2012) NEAT: road network aware trajectory clustering. In: Proceedings of the 32nd IEEE international conference on distributed computing systems, pp 142–151

  • Hung CC, Peng WC, Lee WC (2015) Clustering and aggregating clues of trajectories for mining trajectory patterns and routes. VLDB J 24(2):169–192

    Article  Google Scholar 

  • Igiesias F, Kastner W (2013) Analysis of similarity measures in time series clustering for the discovery of building energy patterns. Energies 6:579–597

    Article  Google Scholar 

  • Jeung HY, Yiu ML, Zhou XF, Jensen CS, Shen HT (2008) Discovery of convoys in trajectory databases. In: Proceedings of the 34th international conference on very large data bases, pp 1068–1080

  • Jeung HY, Yiu ML, Zhou XF, Jensen CS, Shen HT (2008) Discovery of convoys in trajectory databases. J Proc VLDB Endow 1(1):1068–1080

    Article  Google Scholar 

  • Khoshaein V (2014) Trajectory clustering using a variation of Fréchet distance. Doctoral dissertation, University of Ottawa, Ottawa, Canada

  • Kisilevich S, Mansmann F, Nanni M, Rinzivillo S (2010) Spatio-temporal clustering: a survey. Data mining and knowledge discovery handbook, 2nd edn. Springer, Heidelberg, pp 1–22

  • Lee JG, Han JW, Whang KY (2007) Trajectory clustering: a partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD international conference on management of data, Beijing, China, pp 593–604

  • Lee JJ, Kim GJ, Kim MH (2012) Trajectory extraction for abnormal behavior detection in public area. In: Proceedings of the 9th international conference & expo on emerging technologies for a smarter world, pp 1–5

  • Li XL, Han JW, Lee JG, Gonzalez H (2007) Traffic density-based discovery of hot routes in road networks. In: Proceedings of the 10th international conference on advances in spatial and temporal databases, pp 441–459

  • Liao TW (2005) Clustering of time series data—a survey. Pattern Recognit 38:1857–1874

    Article  MATH  Google Scholar 

  • Lin B, Su J (2008) OneWay distance, for shape based similarity search of moving object trajectories. GeoInformatica 12(2):117–142

    Article  Google Scholar 

  • Lu GQ, Kong LF, Wang YP, Tian DX (2014) Vehicle trajectory extraction by simple two-dimensional model matching at low camera angles in intersection. IET Intell Transp Syst 8(7):631–638

    Article  Google Scholar 

  • Manolopoulos Y, Nanopoulos A, Theodoridis Y (2006) R-trees: theory and applications. Springer, New York. ISBN 978-1-85233-977-7

  • Masciari E (2009) A framework for trajectory clustering. Lecture notes in computer science, vol 5659, pp 102–111

  • Michael S, Alex W (2011) Fast and accurate k-means for large datasets, advances in neural information processing systems 24. In: 25th annual conference on neural information processing systems 2011, pp 1–9

  • Michail V, Marios H, Dimitrios G (2006) Indexing multidimensional time-series. Int J Very Large Data Bases 15(1):1–20

    Article  Google Scholar 

  • Mitsch S, Muller A, Retschitzegger W, Salfinger A, Schwinger W (2013) A survey on clustering techniques for situation awareness. In: Proceedings of the 15th Asia-Pacific web conference, pp 815–826

  • Nagesh H, Goil S, Chooudhary A (2001) Adaptive grids for clustering massive data sets. In: Proceedings of the 1st SIAM international conference on data mining, pp 1–17

  • Nanni M, Pedreschi D (2006) Time-focused clustering of trajectories of moving objects. J Intell Inf Syst 27(3):267–289

    Article  Google Scholar 

  • Nock R, Nielsen F (2006) On weighting clustering. IEEE Trans Pattern Anal Mach Intell 28(8):1–13

    Article  Google Scholar 

  • Palma AT, Bogorny V, Kuijpers B, Alvares LO (2008) A clustering-based approach for discovering interesting places in trajectories. In: Proceedings of the 2008 ACM symposium on applied computing, pp 863–868

  • Panagiotakis C, Pelekis N, Kopanakis I, Ramasso E, Theodoridis Y (2012) Segmentation and sampling of moving object trajectories based on representativeness. IEEE Trans Knowl Data Eng 24(7):1328–1343

    Article  Google Scholar 

  • Parent C, Spaccapietra S, Renso C, Andrienko G, Andrienko N, Bogorny V, Damiani ML, Divanis AG, Macedo J, Pelekis N, Theodoridis Y, Yan ZX (2013) Semantic trajectories modeling and analysis. J ACM Comput Surv 45(4):1–37

  • Park HS, Jun CH (2009) A simple and fast algorithm for K-medoids clustering. Expert Syst Appl 36(2):3336–3341

    Article  Google Scholar 

  • Pelekis N, Kopanakis I, Kotsifakos EE, Frentzos E, Theodoridis Y (2011) Clustering uncertain trajectories. Knowl Inf Syst 28(1):117–147

    Article  Google Scholar 

  • Plaue M, Chen MJ, Bärwolff G, Schwandt H (2011) Trajectory extraction and density analysis of intersecting pedestrian flows from video recordings. Lecture notes in computer science, vol 6952, pp 285–296

  • Qian WN, Zhou AY (2002) Analyzing popular clustering algorithms from different viewpoints. J Softw 13(8):1382–1394

    Google Scholar 

  • Rick C (2002) Efficient computation of all longest common subsequences. Lecture notes in computer science, vol 1851, pp 407–418

  • Roh GP, Hwang SW (2010) NNCluster: an efficient clustering algorithm for road network trajectories. In: Proceedings of the 15th international conference on database systems for advanced applications, vol 2, pp 47–61

  • Sankoff D, Kruskal J (1983) Time warps, string edits, and macromolecules: the theory and practice of sequence comparison. Addison-Wesley, MA

    MATH  Google Scholar 

  • Shi J, Tomasi C (1994) Good features to track. In: Proceedings of of the IEEE computer society conference on computer vision and pattern recognition, pp 593–600

  • Tao YF, Papadias D (2001) Efficient historical R-trees. In: Proceedings of the 13th international conference on scientific and statistical database management, pp 223–232

  • Tao YF, Papadias D, Sun JM (2003) The TPR*-tree: an optimized spatio-temporal access method for predictive queries. In: Proceedings of the 29th international conference on very large data bases, vol 29, pp 790–801

  • Tsumoto S, Hirano S (2009) Behavior grouping based on trajectory mining. In: Proceedings of the 2nd international workshop on social computing, behavioral modeling and prediction, Phoenix, AZ, USA, pp 219–226

  • Vlachos M, Kollios G, Gunopulos D (2002) Discovering similar multidimensional trajectories. In: Proceedings of the 18th international conference on data engineering, San Jose, CA, pp 673–684

  • Wang XF, Li G, Jiang G, Shi ZZ (2013) Semantic trajectory-based event detection and event pattern mining. Knowl Inf Syst 37(2):305–329

    Article  Google Scholar 

  • Wang S, Wu L, Zhou F, Zheng C, Wang H (2015) Group pattern mining algorithm of moving objects’ uncertain trajectories. Int J Comput Commun Control 10(3):428–440

    Article  Google Scholar 

  • Wang W, Yang J, Muntz RR (1997) STING: a statistical information grid approach to spatial data mining. In: Proceedings of the 23rd international conference on very large databases, pp 186–195

  • Wei LX, He XH, Teng QZ, Gao ML (2013) Trajectory classification based on Hausdorff distance and longest common subsequence. J Electron Inf Technol 35(4):784–790

    Article  Google Scholar 

  • Wikipedia (2015) DBSCAN, https://en.wikipedia.org/wiki/DBSCAN 2015-11-25

  • Won JI, Kim SW, Baek JH, Lee JH (2009) Trajectory clustering in road network environment. In: Proceedings of the 2009 IEEE symposium on computational intelligence and data mining, pp 299–305

  • Yan ZX, Chakraborty D, Parent C, Spaccapietra S, Abere K (2012) Semantic trajectories: mobility data computation and annotation. ACM Trans Intell Syst Technol 9(4):1–34

  • Yan ZX (2011) Semantic trajectories: computing and understanding mobility data. Doctoral dissertation, Swiss Federal Institute of Technology, Lausanne

  • Yanagisawa Y, Akahani J, Satoch T (2003) Shape-based similarity query for trajectory of mobile objects. In: Proceedings of the 4th international conference on MDM, pp 63–77

  • Yanagisawa Y, Satph T (2006) Clustering multidimensional trajectories based on shape and velocity. In: Proceedings of the 22nd international conference on data engineering workshops, pp 12–21

  • Yasodha M, Ponmuthuramalingam DRP (2012) A survey on temporal data clustering. Int J Adv Res Comput Commun Eng 1(9):772–786

    Google Scholar 

  • Ying JJC, Lee WC, Weng TC, Tseng VS (2011) Semantic trajectory mining for location prediction. In: Proceedings of the 19th ACM SIGSPATIAL GIS, November 1–4, pp 34–43

  • Yuan G, Xia SX, Zhang YM (2013) Interesting activities discovery for moving objects based on collaborative filtering. Math Probl Eng 2013:1–9

  • Yuan G, Xia SX, Zhang L, Zhou Y, Ji C (2012) An efficient trajectory-clustering algorithm based on an index tree. Trans Inst Meas Control 34(7):850–861

    Article  Google Scholar 

  • Zhang Z, Huang K, Tan TN (2006) Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In: Proceedings of the 18th international conference on pattern recognition, vol 3, pp 1135–1138

  • Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering method for very large databases. In: Proceedings of the 1996 ACM SIGMOD international conference on management of data, pp 103–114

  • Zheng Y (2015) Trajectory data mining: an overview. ACM Trans Intell Syst Technol 6(3):1–41

  • Zheng Y, Li Q, Chen Y, Xie X. (2011) Understanding mobility based on GPS data. In: Proceedings of the 13th international conference on ubiquitous computing, ACM, pp 312–321

  • Zhong S, Ghosh J (2003) A unified framework for model-based clustering. J Mach Learn Res 4:1001–1037

    MathSciNet  MATH  Google Scholar 

  • Zhou FC, He XY, Wang S, Xu J, Wang MW, Wu LN (2014) A clustering-based privacy-preserving method for uncertain trajectory data. In: Proceedings of the IEEE 13th international conference on trust, security and privacy in computing and communications, pp 1–8

  • Zhou SG, Zhou AY, Cao J, Hu YF (2000) A fast density-based clustering algorithm. J Comput Res Dev 37(11):1287–1292

    Google Scholar 

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Acknowledgments

This work was supported by the natural science foundation of Jiangsu province, China (with Grant of BK20130208), and the Fundamental Research Funds for the Central Universities, China (with Grant of 2013QNA25).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China

    Guan Yuan, Penghui Sun, Jie Zhao & Daxing Li

  2. Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, China University of Mining and Technology, No. 1 Daxue Road, Xuzhou, 221116, Jiangsu, China

    Guan Yuan

  3. Department of Information and Engineering, Shandong Management University, Jinan, 250357, China

    Canwei Wang

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  1. Guan Yuan
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Correspondence to Guan Yuan.

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Yuan, G., Sun, P., Zhao, J. et al. A review of moving object trajectory clustering algorithms. Artif Intell Rev 47, 123–144 (2017). https://doi.org/10.1007/s10462-016-9477-7

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