Top

Artificial Intelligence Review

Published in:

15-07-2019

Spatiotemporal clustering: a review

Authors: Mohd Yousuf Ansari, Amir Ahmad, Shehroz S. Khan, Gopal Bhushan, Mainuddin

Published in: Artificial Intelligence Review | Issue 4/2020

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

An increase in the size of data repositories of spatiotemporal data has opened up new challenges in the fields of spatiotemporal data analysis and data mining. Foremost among them is “spatiotemporal clustering,” a subfield of data mining that is increasingly becoming popular because of its applications in wide-ranging areas such as engineering, surveillance, transportation, environmental and seismology studies, and mobile data analysis. This review paper presents a comprehensive review of spatiotemporal clustering approaches and their applications as well as a brief tutorial on the taxonomy of data types in the spatiotemporal domain and patterns. Additionally, the data pre-processing techniques, access methods, cluster validation, space–time scan statistics, software tools, and datasets used by various spatiotemporal clustering algorithms are highlighted.

previous article Segmentation of cervical cells for automated screening of cervical cancer: a review

next article Reduction foundation with multigranulation rough sets using discernibility

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Achtert E, Kriegel H, Zimek A (2008) ELKI: a software system for evaluation of subspace clustering algorithms. Scientific and statistical database management (SSDBM), pp 580–585

Agrawal R, Gehrke J, Gunopulos D, Raghavan P (1998) Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings of th ACM SIGMOD’98 international conference on management of data, Seattle, WA, pp 94–105

Agrawal KP, Garg S, Sharma S, Patel P (2016) Development and validation of OPTICS based spatio-temporal clustering technique. Inf Sci (Ny) 369:388–401. https://doi.org/10.1016/j.ins.2016.06.048 CrossRef

Ahmad A, Dey L (2007) A k-mean clustering algorithm for mixed numeric and categorical data. Data Knowl Eng 63:503–527. https://doi.org/10.1016/j.datak.2007.03.016 CrossRef

Alatrista-Salas H, Azé J, Bringay S et al (2015) A knowledge discovery process for spatiotemporal data: application to river water quality monitoring. Ecol Inform 26:127–139. https://doi.org/10.1016/j.ecoinf.2014.05.011 CrossRef

Alon J, Sclaroff S, Kollios G, Pavlovic V (2003) Discovering clusters in motion time-series data. In: 2003 IEEE computer society conference on computer vision and pattern, 2003 proceedings, vol 1, pp I–375. https://doi.org/10.1109/cvpr.2003.1211378

Alvares LO, Palma A, Oliveira G, Bogorny V (2010) Weka-STPM: from trajectory samples to semantic trajectories. Proc Work Open Source Code 1:1–6

Alvares LO, Loy AM, Renso C, Bogorny V (2011) An algorithm to identify avoidance behavior in moving object trajectories. J Braz Comput Soc 17:193–203. https://doi.org/10.1007/s13173-011-0037-3 CrossRef

Anbaroglu B, Cheng T, Heydecker B (2015) Non-recurrent traffic congestion detection on heterogeneous urban road networks. Transportmetrica A Transp Sci 11:754–771. https://doi.org/10.1080/23249935.2015.1087229 CrossRef

Andrienko G, Andrienko N (2008) Spatio-temporal aggregation for visual analysis of movements. In: IEEE symposium visual analytics science and technology, 2008. VAST’08

Andrienko G, Andrienko N (2010) Interactive cluster analysis of diverse types of spatiotemporal data. ACM SIGKDD Explor Newsl 11:19–28. https://doi.org/10.1145/1809400.1809405 CrossRef

Andrienko G, Andrienko N, Wrobel S, Augustin S (2007) Visual analytics tools for analysis of movement data. ACM SIGKDD Explor Newsl 9:38–46. https://doi.org/10.1145/1345448.1345455 CrossRef

Ankerst M, Breunig MM, Kriegel H, Sander J (1999) OPTICS : ordering points to identify the clustering structure. In: SIGMOD’99 proceedings of the1999 ACM SIGMOD international conference on management of data, vol 28, pp 49–60. https://doi.org/10.1145/304182.304187

Arbelaitz O, Gurrutxaga I, Muguerza J et al (2013) An extensive comparative study of cluster validity indices. Pattern Recognit 46:243–256. https://doi.org/10.1016/j.patcog.2012.07.021 CrossRef

Auria M, Nanni M, Pedreschi D (2006) Time-focused dentisty-based clustering of trajectories of moving objects. Spacial issue on mining spatio-tamporal data. JIIS 27:267–289

Baglioni M, de Macêdo JAF, Renso C, Trasarti R, Wachowicz M (2009) Towards semantic interpretation of movement behavior. In: Advances in GIScience, Lecture Notes in Geoinformation and Cartography. Springer, Berlin, pp 271–288. https://doi.org/10.1007/978-3-642-00318-9_14

Ball GH, Hall DJ (1965) ISODATA, a novel method of data analysis and pattern classification. Stanford Research Institute, Menlo Park

Becher J, Berkhin P, Freeman E (2000) Automating exploratory data analysis for efficient data mining. In: Proceedings of the 6th ACM SIGKDD, pp 424–429

Beckmann N, Begel H-P, Schneider R, Seeger B (1990) The R*-tree: an efficient and robust access method for points and rectangles. ACM SIGMOD Record 19(2):322–331CrossRef

Bentley JL (1975) Multidimensional binary search trees used for associative searching. Commun ACM 18:509–517CrossRef

Berkhin P (2006) Survey of clustering data mining techniques. Group Multidimens Data Recent Adv Clust. https://doi.org/10.1007/3-540-28349-8_2 CrossRef

Bernárdez FDP (2016) Extraction of user’s stay and transitions from GPS logs: a comparison of three spatiotemporal clustering approaches. Master Thesis, Institute for Geo-information, Vienna University of Technology

Birant D, Kut A (2007) ST-DBSCAN: an algorithm for clustering spatial-temporal data. Data Knowl Eng 60:208–221. https://doi.org/10.1016/j.datak.2006.01.013 CrossRef

Bogorny V, Shashi S (2010) Spatial and spatio-temporal data mining. In: 2010 IEEE international conference on data mining

Cai M, Revesz PZ (2000) Parametric R-Tree: an index structure for moving objects. In: Proc. of the COMAD Conf

Calinski T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat Theory Methods 3(1):1–27MathSciNetCrossRef

Campello RJGB, Moulavi D, Sander J (2013) Density-based clustering based on hierarchical density estimates. In: Pei J, Tseng VS, Cao L, Motoda H, Xu G (eds) Advances in knowledge discovery and data mining. PAKDD 2013(Lecture notes in computer science), Springer, Berlin, vol 7819, pp 160–172

Cao H, Mamoulis N, Cheung DW (2005) Mining frequent spatio-temporal sequential patterns. In: ICDM: Proceedings of the fifth IEEE international conference on datamining (2005). IEEE Computer Society Press, pp 82–89. https://doi.org/10.1109/icdm.2005.95

Chen XC, Faghmous JH, Khandelwal A, Kumar V (2015) Clustering dynamic spatio-temporal patterns in the presence of noise and missing data. In: International joint conference on artificial intelligence, pp 2575–2581

Chudova D, Gaffney S, Mjolsness E, Smyth P (2003) Translation-invariant mixture models for curve clustering. In: Proc ninth ACM SIGKDD int conf knowl discov data min—KDD’03 79. https://doi.org/10.1145/956750.956763

Clementini E, Felice PD, Oosterom PV (1993) A small set of formal topological relationships suitable for end-user interaction. In: Advances in spatial databases

Compieta P, Di Martino S, Bertolotto M, Ferrucci F, Kechadi T (2007) Exploratory spatio-temporal data mining and visualization. J Vis Lang Comput 18:255–279CrossRef

Dataset Search—Google https://toolbox.google.com/datasetsearch. Accessed 05 Feb 2019

De Lucca Siqueira F, Bogorny V (2011) Discovering chasing behavior in moving object trajectories. Trans GIS 15:667–688. https://doi.org/10.1111/j.1467-9671.2011.01285.x CrossRef

Doborjeh M G, Kasabov N (2015) Dynamic 3D clustering of spatio-temporal brain data in the NeuCube spiking neural network architecture on a case study of fMRI data. In: International conference on neural information processing. Springer, Cham, pp 191–198. https://doi.org/10.1007/978-3-319-26561-2_23

Doborjeh MG, Kasabov N, Doborjeh ZG (2018) Evolving, dynamic clustering of spatio/spectro-temporal data in 3D spiking neural network models and a case study on EEG data. Evolv Syst 9(3):195–211CrossRef

Dunn JC (1974) Well separated clusters and optimal fuzzy partitions. J Cybern 4:95–105MathSciNetCrossRef

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

Faghmous JH, Kumar V (2014) A big data guide to understanding climate change: the case for theory-guided data science. Big Data 2(3):155–163. https://doi.org/10.1089/big.2014.0026 CrossRef

Fiori A, Mignone A, Rospo G (2016) DeCoClu: density consensus clustering approach for public transport data. Inf Sci 328:378–388CrossRef

Gaffney S, Smyth P (1999) Trajectory clustering with mixtures of regression models. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining KDD 99, vol 10, pp 63–72. https://doi.org/10.1145/312129.312198

Georgoulas G, Konstantaras A, Katsifarakis E et al (2013) “Seismic-mass” density-based algorithm for spatio-temporal clustering. Expert Syst Appl 40:4183–4189. https://doi.org/10.1016/j.eswa.2013.01.028 CrossRef

Giannotti F, Pedreschi D (2008) Mobility, data mining and privacy: geographic knowledge discovery. Springer, HeidelbergCrossRef

Giannotti F, Nanni M, Pinelli F, Pedreschi D (2007) Trajectory pattern mining. In: Proceedings of the ACM KDD, pp. 330–339. https://doi.org/10.1145/1281192.1281230

Giannotti F, Kujpers B, Raffaeta A, Manco G, Baglioni M, Renso C (2008) Querying and reasoning for spatio-temporal data mining. In: Giannotti F, Pedreschi D (eds) Mobility, data mining and privacy. Springer, Berlin, pp 335–374. https://doi.org/10.1007/978-3-540-75177-9_13 CrossRef

Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of Things (IoT): a vision, architectural elements, and future directions. Futur Gener Comput Syst 29:1645–1660. https://doi.org/10.1016/j.future.2013.01.010 CrossRef

Güting RH (1994) An introduction to spatial database systems. VLDB J 3(4):357–399. https://doi.org/10.1007/bf01231602 CrossRef

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—SIGMOD’84, pp 47–57. https://doi.org/10.1145/602259.602266

Halkidi M, Batistakis Y, Vazirgiannis M (2001) On clustering validation techniques. J Intell Inf Syst 17:107–145. https://doi.org/10.1023/a:1012801612483 CrossRefMATH

Hall M, Frank E, Holmes G et al (2009) The WEKA data mining software. ACM SIGKDD Explor 11:10–18. https://doi.org/10.1145/1656274.1656278 CrossRef

Han T, Yao H, Sun X et al (2016) Unsupervised discovery of crowd activities by saliency-based clustering. Neurocomputing 171:347–361. https://doi.org/10.1016/j.neucom.2015.06.048 CrossRef

Henrich A, Six H-W, Widmayer P (1989). The LSD-tree: spatial access to multidimensional point- and non-point-objects. In: Proceedings of 15th international conference on very large data bases, Amsterdam, pp 45–53

Higgs B, Abbas M (2015) Segmentation and clustering of car-following behavior: recognition of driving patterns. IEEE Trans Intell Transp Syst 16(1):81–90CrossRef

Hu B, Jamali M, Ester M (2013) Spatio-temporal topic modeling in mobile social media for location recommendation. In: Proceedings—IEEE international conference on data mining, ICDM, pp 1073–1078. https://doi.org/10.1109/icdm.2013.139

Huang W, Xu S, Yan Y, Zipf A (2019) An exploration of the interaction between urban human activities and daily traffic conditions: a case study of Toronto, Canada. Cities 84:8–22. https://doi.org/10.1016/j.cities.2018.07.001 CrossRef

Hudjimartsu SA, Djatna T, Ambarwari A, Apriliantona (2018) Spatial temporal clustering for hotspot using Kulldorff scan statistic method (KSS): a case in Riau Province. In: IOP conference series: earth and environmental science, vol 54, pp 012056. http://iopscience.iop.org/article/10.1088/1755-1315/54/1/012056

Husch M, Schyska BU, Bremen LV (2018) CorClustST—correlation-based clustering of big spatio-temporal datasets. Future Gener Comput Syst 100:100. https://doi.org/10.1016/j.future.2018.04.002(In Press) CrossRef

Hwang SY, Liu YH, Chiu JK, Lim EP (2005) Mining mobile group patterns: a trajectory-based approach. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, pp 713–718

Izakian H, Pedrycz W (2013) Anomaly detection in time series data using a fuzzy C-means clustering. In: IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), 2013 Joint. IEEE, pp 1513–1518

Izakian H, Pedrycz W, Jamal I (2013) Clustering spatiotemporal data: an augmented fuzzy c-means. IEEE Trans Fuzzy Syst 21:855–868CrossRef

Izakian H, Pedrycz W, Jamal I (2015) Fuzzy clustering of time series data using dynamic time warping distance. Eng Appl Artif Intell 39:235–244. https://doi.org/10.1016/j.engappai.2014.12.015 CrossRef

Jeung H, Yiu ML, Zhou X, et al (2008) Discovery of convoys in trajectory databases. In: Proceedings of the VLDB endowment, vol 1, pp 1068–1080. https://doi.org/10.14778/1453856.1453971

Kalnis P, Mamoulis N, Bakiras S (2005) On discovering moving clusters in spatio-temporal data. Adv Spat Temporal Databases SE 21(3633):364–381. https://doi.org/10.1007/11535331_21 CrossRef

Kalyani D, Chaturvedi SK (2012) A survey on spatio-temporal data mining. Int J Comput Sci Netw 1(4):1–7

Kang J, Yong H (2009) Mining trajectory patterns by incorporating temporal properties. In: Proceedings of the 1st international conference on emerging databases, pp 1–6

Kang JH, Welbourne W, Stewart B, Borriello G (2005) Extracting places from traces of locations. ACM SIGMOBILE Mob Comput Commun Rev 9:58. https://doi.org/10.1145/1094549.1094558 CrossRef

Kirana AP, Sitanggang IS, Syaufina L(2016) Hotspot pattern distribution in peat land area in Sumatera based on spatio temporal clustering. In: The 2nd international symposium on LAPAN-IPB satellite for food security and environmental monitoring 2015, LISAT-FSEM 2015, vol 33, pp 635–645

Kisilevieh S, Mansmann F, Nanni M, Rinzivillo S (2010) Spatio-temporal c1ustering. In: Data mining and knowledge discovery handbook, pp 855–874

Kovács F, Legány C, Babos A (2005) Cluster validity measurement techniques. In: Proc 6th int symp hungarian res comput intell 2006, pp 1–11. https://doi.org/10.7547/87507315-91-9-465

Kulldorff M (1997) A spatial scan statistic. Commun Stat Theory Methods 26:1481–1496MathSciNetCrossRef

Kulldorff M (2018) SaTScan user guide for version 9.6. https://www.satscan.org. Accessed 20 June 2018

Kulldorff M, Nagarwalla N (1995) Spatial disease clusters: detection and inference. Stat Med 14:799–810. https://doi.org/10.1002/sim.4780140809 CrossRef

Larose DT (2005) Discovering knowledge in data: an introduction to data mining. Wiley, HobokenMATH

Lee CH (2012) Mining spatio-temporal information on microblogging streams using a density-based online clustering method. Expert Syst Appl 39:9623–9641. https://doi.org/10.1016/j.eswa.2012.02.136 CrossRef

Lee J, Han J, Whang K-Y (2007) Trajectory clustering: a partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD international conference on management of data—SIGMOD’07, pp 593. https://doi.org/10.1145/1247480.1247546

Leipnik MR, Albert DP (2002) GIS in law enforcement: implementation issues and case studies. CRC Press, SacramentoCrossRef

Li Y, Han J, Yang J (2004) Clustering moving objects. In: Proceedings of the 2004 ACM SIGKDD international conference on knowledge discovery and data mining—KDD’04, pp 617–622. https://doi.org/10.1145/1014052.1014129

Liu Y, Li Z, Xiong H, Gao X, Gao J (2010) Understanding of internal clustering validation measures. In: Proceedings of the 2010 IEEE international conference on data mining. pp 911–916. https://doi.org/10.1109/icdm.2010.35

Liu J, Xue C, He Y, Dong Q, Kong F, Hong Y (2018) Dual-constraint spatiotemporal clustering approach for exploring marine anomaly patterns using remote sensing products. IEEE J Sel Top Appl Earth Obs Remote Sens 11(11):3963–3976. https://doi.org/10.1109/jstars.2018.2873216 CrossRef

Maciag PS (2017) A survey on data mining methods for clustering complex spatiotemporal data. In: International conference: beyond databases, architectures and structures, pp 115–126. https://doi.org/10.1007/978-3-319-58274-0

Macqueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth berkeley symposium on mathematical statistics and probability, vol 1, pp 281–297. https://projecteuclid.org/euclid.bsmsp/1200512992

Manjula A, Narsimha G (2014) A review on spatial data mining methods and applications. Int J Comput Eng Appl VII:208–218

Martino FD, Pedrycz W, Sessa S (2018) Spatiotemporal extended fuzzy C-means clustering algorithm for hotspots detection and prediction. Fuzzy Sets Syst 340:109–126MathSciNetCrossRef

Mazimpaka JD, Timpf S (2016) Trajectory data mining: a review of methods and applications. J Spat Inf Sci 13:61–99. https://doi.org/10.5311/josis.2016.13.263 CrossRef

Mikut R, Reischl M (2011) Data mining tools. Wiley Interdiscip Rev Data Min Knowl Discov 1:431–443. https://doi.org/10.1002/widm.24 CrossRef

Miller HJ, Han J (2005) Geographic data mining and knowledge discovery, vol 2. CRC Press, Boca Raton

Milligan GW (1981) A monte carlo study of thirty internal criterion measures for cluster analysis. Psychometrika 46:187–199. https://doi.org/10.1007/bf02293899 CrossRefMATH

MOD44W | LP DAAC :: NASA land data products and services. https://lpdaac.usgs.gov › MODIS › MODIS Products Table. Accessed 22 Dec 2016

Murray C (2013) Oracle spatial developer’s guide, 11g release 2 (11.2) E11830-15

Neill DB (2006) Detection of spatial and spatio-temporal clusters. PhD Thesis. School of Computer Science,Carnegie Mellon University, Pittsburgh. https://www.cs.cmu.edu/~neill/papers/thesis-final.pdf. Accessed 25 June 2018

NYC Taxi & Limousine Commission. http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml. Accessed 21 June 2018

Obe RO, Hsu LS (2015) PostGIS in action, 2nd edn. Manning Publications Co., Shelter Island, NY, p 11964

Otair M (2013) Approximate k-nearest neighbor based spatial clustering using kd- tree. Int J Database Manag Syst 5(1):97–108CrossRef

Palma AT, Bogorny V, Kuijpers B, Alvares LO (2008). A clustering based approach for discovering interesting places in trajectories. In: ACMSAC. ACM Press, New York, pp 863–868

Pearson K (1895) Note on regression and inheritance in the case of two parents. Proc R Soc Lond Ser I 58:240–242CrossRef

Pelekis N, Kopanakis I, Marketos G, et al (2007) Similarity search in trajectory databases. In: Proceedings of the international workshop on temporal representation and reasoning, pp 129–140. https://doi.org/10.1109/time.2007.59

Plenar.io-A spatio-temporal open data platform http://plenar.io. Accessed 05 Feb 2019

Rendón E, Abundez I, Arizmendi A, Quiroz EM (2011) Internal versus external cluster validation indexes. Int J Comput Commun 5(1):27–34

Rinzivillo S, Pedreschi D, Nanni M et al (2008) Visually driven analysis of movement data by progressive clustering. Inf Vis 7:225–239. https://doi.org/10.1057/palgrave.ivs.9500183 CrossRef

Rocha JAMR, Times VC, Oliveira G, et al (2010) DB-SMoT: a direction-based spatio-temporal clustering method. In: 2010 5th IEEE international conference intelligent systems, pp 114–119. https://doi.org/10.1109/is.2010.5548396

Salazar GEJ, Velez AC, Parra MCM, Ortega LO (2002) A cluster validity index for comparing non-hierarchical clustering methods. EITI 2002:1–5

Samet H (1985) Using quadtree to represent spatial data. NATO ASI Ser F18:229–247

Sander J, Ester M, Kriegel HPP, Xu X (1998) Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Min Knowl Disc 2(2):169–194. https://doi.org/10.1023/a:1009745219419 CrossRef

Sardadi MM, Shafry M, Jupri Z, Daman D (2008) Choosing R-tree or quadtree spatial data indexing in one oracle spatial database system to make faster showing geographical map in mobile geographical information system technology. World Academy of Science, Engineering and Technology, Paris, pp 249–257

Sellis TK, Roussopoulos N, Faloutsos C (1987) The R+-Tree: a dynamic index for multi- dimensional objects. In: VLDB, pp 507–518

Shekhar S, Varsavai RR, Celik M (2008) Spatial and spatiotemporal data mining: recent advances. Data mining: next generation challenges and future directions, AAAI Press

Shekhar S, Jiang Z, Ali RY et al (2015) Spatiotemporal data mining: a computational perspective. ISPRS Int J Geo-Inf 4:2306–2338. https://doi.org/10.3390/ijgi4042306 CrossRef

Spaccapietra S, Parent C, Damiani ML, De Macedo JA, Porto F, Vangenot C (2008) A conceptual view on trajectories. Data Knowl Eng 65:126–146CrossRef

SRTM Water Body Dataset | The long term archive. https://lta.cr.usgs.gov/srtm_water_body_dataset. Accessed 14 March 2017

The R project for statistical computing. https://www.r-project.org. Accessed 15 Feb 2017

Tork HF (2012) Spatio-temporal clustering methods classification. In: Doctoral symposium on informatics engineering (DSIE’2012), pp 1–12. https://doi.org/10.13140/rg.2.1.3812.7204

UNISYS, Atlantic tropical storm tracking by year. http://weather.unisys.com/hurricane/atlantic/. Accessed 11 March 2017

Upton G, Fingleton B (1985) Spatial data analysis by example volume1: point pattern and quantitative data. Wiley, New YorkMATH

Vieira MR, Bakalov P, Tsotras VJ (2009) On-line discovery of flock patterns in spatio-temporal data. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems—GIS’09 286. https://doi.org/10.1145/1653771.1653812

Wang X, Wang J (2010) Using clustering methods in geospatial information systems. GEOMATICA 64:347–361

Wang M., Wang A, Li A (2006) Mining spatial-temporal clusters from geo-databases. In: International conference on advanced data mining and applications. Springer Berlin, pp 263–270

Weingessel A, Dimitriadou E, Dolničar S (1999) An examination of indexes for determining the number of clusters in binary data sets. Psychometrika 67:1–21. https://doi.org/10.1007/bf02294713 MathSciNetCrossRefMATH

Y—U.S. Forest Service https://www.fs.fed.us/pnw/starkey/. Accessed 7 Dec 2016

Yao X (2003). Research issues in spatio-temporal data mining. A white paper submitted to the University Consortium for Geographic Information Science (UCGIS) workshop on geospatial visualization and knowledge discovery, Lansdowne, Virginia, Nov. 18–20

Zaghlool E, ElKaffas S, Saad, A (2015) A density-based clustering of spatio-temporal data. In: Rocha A, Correia A, Costanzo S, Reis L (eds) New contributions in information systems and technologies. Advances in Intelligent Systems and Computing, vol 354. Springer, Cham, pp 41–50. https://doi.org/10.1007/978-3-319-16528-8_5 CrossRef

Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering databases method for very large. ACM SIGMOD Int Conf Manag Data 1:103–114. https://doi.org/10.1145/233269.233324 CrossRef

Zhang P, Huang Y, Shekhar S, Kumar V (2003) Correlation analysis of spatial time series datasets: a filter-and-refine approach. Adv Knowl Discov Data Mining. https://doi.org/10.1007/3-540-36175-8_53 CrossRefMATH

Zhang D, Lee K, Lee I (2018) Hierarchical trajectory clustering for spatio-temporal periodic pattern mining. Expert Syst Appl 92:1–11CrossRef

Zhao Q, Xu M, Fränti P (2009) Sum-of-square based cluster validity index and significance analysis. In: Proceedings of the 17th international conference on adaptive structures and natural computing algorithms, pp 313–322

Zheng Y, Zhang L, Xie X, Ma W-Y (2009) Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th international conference on World wide web - WWW’09 791. https://doi.org/10.1145/1526709.1526816

Zhou P, Salzberg B (2008) The hB-pi* Tree: an optimized comprehensive access method for frequent-update multi-dimensional point data. In: Ludäscher B., Mamoulis N. (eds) Scientific and statistical database management. SSDBM 2008 (Lecture notes in computer science), vol 5069. Springer, Berlin

Title: Spatiotemporal clustering: a review
Authors: Mohd Yousuf Ansari
Amir Ahmad
Shehroz S. Khan
Gopal Bhushan
Mainuddin
Publication date: 15-07-2019
Publisher: Springer Netherlands
Published in: Artificial Intelligence Review / Issue 4/2020
Print ISSN: 0269-2821
Electronic ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-019-09736-1

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 4/2020

On the frontiers of pose invariant face recognition: a review

Recent trends in deep learning based personality detection

Feature selection in image analysis: a survey

Flexible slider crank mechanism synthesis using meta-heuristic optimization techniques: a new designer tool assistance for a compliant mechanism synthesis

A survey of feature extraction and fusion of deep learning for detection of abnormalities in video endoscopy of gastrointestinal-tract

A systematic review of fundamental and technical analysis of stock market predictions

Premium Partner