Spatiotemporal clustering: a review

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.

[1]  P. Fränti,et al.  Sum-of-Squares Based Cluster Validity Index and Significance Analysis , 2009, ICANNGA.

[2]  Dimitrios Gunopulos,et al.  Automatic subspace clustering of high dimensional data for data mining applications , 1998, SIGMOD '98.

[3]  Daut Daman,et al.  Choosing R-tree or Quadtree Spatial DataIndexing in One Oracle Spatial Database System to Make Faster Showing Geographical Map in Mobile Geographical Information System Technology , 2008 .

[4]  Miriam Baglioni,et al.  Towards Semantic Interpretation of Movement Behavior , 2009, AGILE Conf..

[5]  Edmund Freeman,et al.  Automating exploratory data analysis for efficient data mining , 2000, KDD '00.

[6]  Vania Bogorny,et al.  Spatial and Spatio-temporal Data Mining , 2008, 2010 IEEE International Conference on Data Mining.

[7]  Dariusz Mrozek,et al.  Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation - 13th International Conference, BDAS 2017, Ustroń, Poland, May 30 - June 2, 2017, Proceedings , 2017, BDAS.

[8]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[9]  Vania Bogorny,et al.  Weka-STPM : from trajectory samples to semantic trajectories , 2010 .

[10]  Gennady L. Andrienko,et al.  Spatio-temporal aggregation for visual analysis of movements , 2008, 2008 IEEE Symposium on Visual Analytics Science and Technology.

[11]  Vladimir Pavlovic,et al.  Discovering clusters in motion time-series data , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[12]  Lueder von Bremen,et al.  CorClustST - Correlation-based clustering of big spatio-temporal datasets , 2020, Future Gener. Comput. Syst..

[13]  Maryam Gholami Doborjeh,et al.  Evolving, dynamic clustering of spatio/spectro-temporal data in 3D spiking neural network models and a case study on EEG data , 2018, Evol. Syst..

[14]  Juyoung Kang,et al.  Mining Trajectory Patterns by Incorporating Temporal Properties , 2009 .

[15]  Min Wang,et al.  Mining Spatial-temporal Clusters from Geo-databases , 2006, ADMA.

[16]  Jiawei Han,et al.  Geographic Data Mining and Knowledge Discovery , 2001 .

[17]  SeegerBernhard,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990 .

[18]  Witold Pedrycz,et al.  Anomaly detection in time series data using a fuzzy c-means clustering , 2013, 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS).

[19]  Piotr S. Maciag,et al.  A Survey on Data Mining Methods for Clustering Complex Spatiotemporal Data , 2017, BDAS.

[20]  G. ErikaJohanaSalazar,et al.  A Cluster Validity Index for Comparing Non-hierarchical Clustering Methods , 2002 .

[21]  Hadi Fanaee-T.  Spatio-temporal clustering methods classification , 2012 .

[22]  Yan Huang,et al.  Correlation Analysis of Spatial Time Series Datasets: A Filter-and-Refine Approach , 2003, PAKDD.

[23]  Padhraic Smyth,et al.  Translation-invariant mixture models for curve clustering , 2003, KDD '03.

[24]  Regina O. Obe,et al.  PostGIS in Action , 2011 .

[25]  GunopulosDimitrios,et al.  Automatic subspace clustering of high dimensional data for data mining applications , 1998 .

[26]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[27]  Montasir M. Abbas,et al.  Segmentation and Clustering of Car-Following Behavior: Recognition of Driving Patterns , 2015, IEEE Transactions on Intelligent Transportation Systems.

[28]  Hongxun Yao,et al.  Unsupervised discovery of crowd activities by saliency-based clustering , 2016, Neurocomputing.

[29]  Spencer Chainey,et al.  GIS in law enforcement: implementation issues and case studies , 2004 .

[30]  Eliseo Clementini,et al.  A Small Set of Formal Topological Relationships Suitable for End-User Interaction , 1993, SSD.

[31]  Olatz Arbelaitz,et al.  An extensive comparative study of cluster validity indices , 2013, Pattern Recognit..

[32]  C. Pipper,et al.  [''R"--project for statistical computing]. , 2008, Ugeskrift for laeger.

[33]  Dino Pedreschi,et al.  Trajectory pattern mining , 2007, KDD '07.

[34]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.

[35]  Howard J. Hamilton,et al.  Using clustering methods in geospatial information systems , 2009, Geoinformatics.

[36]  Michalis Vazirgiannis,et al.  On Clustering Validation Techniques , 2001, Journal of Intelligent Information Systems.

[37]  S. Dolnicar,et al.  An examination of indexes for determining the number of clusters in binary data sets , 2002, Psychometrika.

[38]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[39]  K. Pearson.  VII. Note on regression and inheritance in the case of two parents , 1895, Proceedings of the Royal Society of London.

[40]  Maguelonne Teisseire,et al.  A knowledge discovery process for spatiotemporal data: Application to river water quality monitoring , 2015, Ecol. Informatics.

[41]  T. Caliński,et al.  A dendrite method for cluster analysis , 1974 .

[42]  Gaetano Borriello,et al.  Extracting places from traces of locations , 2004, MOCO.

[43]  Csaba Legány,et al.  Cluster validity measurement techniques , 2006 .

[44]  Vania Bogorny,et al.  Discovering Chasing Behavior in Moving Object Trajectories , 2011, Trans. GIS.

[45]  Chung-Hong Lee,et al.  Mining spatio-temporal information on microblogging streams using a density-based online clustering method , 2012, Expert Syst. Appl..

[46]  Shashi Shekhar,et al.  Spatiotemporal Data Mining: A Computational Perspective , 2015, ISPRS Int. J. Geo Inf..

[47]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[48]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[49]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[50]  Gennady L. Andrienko,et al.  Interactive cluster analysis of diverse types of spatiotemporal data , 2010, SKDD.

[51]  Hans-Peter Kriegel,et al.  Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications , 1998, Data Mining and Knowledge Discovery.

[52]  Ralf Hartmut Güting Dr.rer.nat.  An introduction to spatial database systems , 2005, The VLDB Journal.

[53]  Panos Kalnis,et al.  On Discovering Moving Clusters in Spatio-temporal Data , 2005, SSTD.

[54]  Sanjay Garg,et al.  Development and validation of OPTICS based spatio-temporal clustering technique , 2016, Inf. Sci..

[55]  Elke Achtert,et al.  ELKI: A Software System for Evaluation of Subspace Clustering Algorithms , 2008, SSDBM.

[56]  Maryam Gholami Doborjeh,et al.  Dynamic 3D Clustering of Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network Architecture on a Case Study of fMRI Data , 2015, ICONIP.

[57]  Witold Pedrycz,et al.  Spatiotemporal extended fuzzy C-means clustering algorithm for hotspots detection and prediction , 2017, Fuzzy Sets Syst..

[58]  H WittenIan,et al.  The WEKA data mining software , 2009 .

[59]  Peter Widmayer,et al.  The LSD tree: spatial access to multidimensional and non-point objects , 1989, VLDB 1989.

[60]  Xing Xie,et al.  Mining interesting locations and travel sequences from GPS trajectories , 2009, WWW '09.

[61]  Vania Bogorny,et al.  A clustering-based approach for discovering interesting places in trajectories , 2008, SAC '08.

[62]  Derya Birant,et al.  ST-DBSCAN: An algorithm for clustering spatial-temporal data , 2007, Data Knowl. Eng..

[63]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[64]  G. W. Milligan,et al.  A monte carlo study of thirty internal criterion measures for cluster analysis , 1981 .

[65]  Hanan Samet,et al.  Using Quadtrees to Represent Spatial Data , 1985 .

[66]  Ee-Peng Lim,et al.  Mining Mobile Group Patterns: A Trajectory-Based Approach , 2005, PAKDD.

[67]  Mohammed Otair,et al.  Approximate k-nearest neighbour based spatial clustering using k-d tree , 2013, ArXiv.

[68]  Hui Xiong,et al.  Understanding of Internal Clustering Validation Measures , 2010, 2010 IEEE International Conference on Data Mining.

[69]  Daut Daman,et al.  Choosing R-tree or Quadtree Spatial Data Indexing in One Oracle Spatial Database , 2009 .

[70]  Jae-Gil Lee,et al.  Trajectory clustering: a partition-and-group framework , 2007, SIGMOD '07.

[71]  James H. Faghmous,et al.  A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science , 2014, Big Data.

[72]  Petko Bakalov,et al.  On-line discovery of flock patterns in spatio-temporal data , 2009, GIS.

[73]  Stefan Wrobel,et al.  Visual analytics tools for analysis of movement data , 2007, SKDD.

[74]  R. M. Cormack,et al.  Spatial Data Analysis by Example. Volume 1: Point Pattern and Quantitative Data , 1985 .

[75]  Dr. G. Narsimha,et al.  A REVIEW ON SPATIAL DATA MINING METHODS AND APPLICATIONS , 2014 .

[76]  Markus Reischl,et al.  Data mining tools , 2011, WIREs Data Mining Knowl. Discov..

[77]  K. Kirby,et al.  Subtalar joint axis location and rotational equilibrium theory of foot function. , 2001, Journal of the American Podiatric Medical Association.

[78]  Alexander Zipf,et al.  An exploration of the interaction between urban human activities and daily traffic conditions: A case study of Toronto, Canada , 2019, Cities.

[79]  Pavel Berkhin,et al.  A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.

[80]  Michela Bertolotto,et al.  Exploratory spatio-temporal data mining and visualization , 2007, J. Vis. Lang. Comput..

[81]  Dino Pedreschi,et al.  Mobility, Data Mining and Privacy - Geographic Knowledge Discovery , 2008, Mobility, Data Mining and Privacy.

[82]  Valéria Cesário Times,et al.  DB-SMoT: A direction-based spatio-temporal clustering method , 2010, 2010 5th IEEE International Conference Intelligent Systems.

[83]  Christian S. Jensen,et al.  Discovery of convoys in trajectory databases , 2008, Proc. VLDB Endow..

[84]  Nikos Mamoulis,et al.  Mining frequent spatio-temporal sequential patterns , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[85]  M Kulldorff,et al.  Spatial disease clusters: detection and inference. , 1995, Statistics in medicine.

[86]  Witold Pedrycz,et al.  Clustering Spatiotemporal Data: An Augmented Fuzzy C-Means , 2013, IEEE Transactions on Fuzzy Systems.

[87]  Vipin Kumar,et al.  Clustering Dynamic Spatio-Temporal Patterns in The Presence of Noise and Missing Data , 2015, IJCAI.

[88]  Sabine Timpf,et al.  Trajectory data mining: A review of methods and applications , 2016, J. Spatial Inf. Sci..

[89]  Dino Pedreschi,et al.  Visually driven analysis of movement data by progressive clustering , 2008, Inf. Vis..

[90]  Hans-Werner Six,et al.  The LSD tree: Spatial Access to Multidimensional Point and Nonpoint Objects , 1989, VLDB.

[91]  Mengchu Cai,et al.  Parametric R-Tree: An Index Structure for Moving Objects , 2000 .

[92]  Lailan Syaufina,et al.  Hotspot Pattern Distribution in Peat Land Area in Sumatera Based on Spatio Temporal Clustering , 2016 .

[93]  Geoffrey H. Ball,et al.  ISODATA, A NOVEL METHOD OF DATA ANALYSIS AND PATTERN CLASSIFICATION , 1965 .

[94]  J. Dunn.  Well-Separated Clusters and Optimal Fuzzy Partitions , 1974 .

[95]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[96]  Hans-Peter Kriegel,et al.  OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.

[97]  Qing Dong,et al.  Dual-Constraint Spatiotemporal Clustering Approach for Exploring Marine Anomaly Patterns Using Remote Sensing Products , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[98]  Ickjai Lee,et al.  Hierarchical trajectory clustering for spatio-temporal periodic pattern mining , 2018, Expert Syst. Appl..

[99]  Lipika Dey,et al.  A k-mean clustering algorithm for mixed numeric and categorical data , 2007, Data Knowl. Eng..

[100]  Amani Saad,et al.  A Density-Based Clustering of Spatio-Temporal Data , 2015, WorldCIST.

[101]  Yifan Li,et al.  Clustering moving objects , 2004, KDD.

[102]  Padhraic Smyth,et al.  Trajectory clustering with mixtures of regression models , 1999, KDD '99.

[103]  Fabio Porto,et al.  A conceptual view on trajectories , 2008, Data Knowl. Eng..

[104]  Vania Bogorny,et al.  An algorithm to identify avoidance behavior in moving object trajectories , 2011, Journal of the Brazilian Computer Society.

[105]  Christos Faloutsos,et al.  The R+-Tree: A Dynamic Index for Multi-Dimensional Objects , 1987, VLDB.

[106]  Shashi Shekhar,et al.  Spatial and Spatiotemporal Data Mining: Recent Advances , 2008 .

[107]  Dino Pedreschi,et al.  Time-focused clustering of trajectories of moving objects , 2006, Journal of Intelligent Information Systems.

[108]  Nikos Pelekis,et al.  Similarity Search in Trajectory Databases , 2007, 14th International Symposium on Temporal Representation and Reasoning (TIME'07).

[109]  Eréndira Rendón,et al.  Internal versus External cluster validation indexes , 2011 .

[110]  Ricardo J. G. B. Campello,et al.  Density-Based Clustering Based on Hierarchical Density Estimates , 2013, PAKDD.

[111]  MikutRalf,et al.  Data mining tools , 2011 .

[112]  Andrew W. Moore,et al.  Detection of spatial and spatio-temporal clusters , 2006 .

[113]  Hans-Peter Kriegel,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990, SIGMOD '90.

[114]  Xuefeng Ya.  Research issues in spatio-temporal data mining , 2003 .

[115]  Panfeng Zhou,et al.  The hB-pi* Tree: An Optimized Comprehensive Access Method for Frequent-Update Multi-dimensional Point Data , 2008, SSDBM.

[116]  Pemetaan Jumlah Balita,et al.  Spatial Scan Statistic , 2014, Encyclopedia of Social Network Analysis and Mining.

[117]  T Djatna,et al.  Spatial temporal clustering for hotspot using kulldorff scan statistic method (KSS): A case in Riau Province , 2017 .

[118]  B Anbaroglu,et al.  Non-recurrent traffic congestion detection on heterogeneous urban road networks , 2015 .

[119]  George J. Vachtsevanos,et al.  "Seismic-mass" density-based algorithm for spatio-temporal clustering , 2013, Expert Syst. Appl..

[120]  Bo Hu,et al.  Spatio-Temporal Topic Modeling in Mobile Social Media for Location Recommendation , 2013, 2013 IEEE 13th International Conference on Data Mining.

[121]  Witold Pedrycz,et al.  Fuzzy clustering of time series data using dynamic time warping distance , 2015, Eng. Appl. Artif. Intell..

[122]  Alessandro Fiori,et al.  DeCoClu: Density consensus clustering approach for public transport data , 2016, Inf. Sci..

[123]  Miriam Baglioni,et al.  Querying and Reasoning for Spatiotemporal Data Mining , 2008, Mobility, Data Mining and Privacy.