Visual mining of moving flock patterns in large spatio-temporal data sets using a frequent pattern approach

References

• Agrawal, R. and Srikant, R., 1994. Fast algorithms for mining association rules. In: J.B. Bocca, M. Jarke, and C. Zaniolo, eds. VLDB’94, Proceedings of the 20th international conference very large data bases, 12–15 September, Santiago de Chile. Morgan Kaufmann, 487–499. ISBN:1-55860-153-8.
   Google Scholar
• Andrienko, G., et al., 2011. A conceptual framework and taxonomy of techniques for analyzing movement. Journal of Visual Languages & Computing, 22, 213–232. doi:10.1016/j.jvlc.2011.02.003
   Web of Science ®Google Scholar
• Ballantyne, J. and Long, D., 2002. A multidecadal study of the number of Antarctic icebergs using scatterometer data. In: Proceedings of the international geoscience and remote sensing symposium, Vol. 5, 24–28 June, Toronto, ON. IEEE Publications, 3029–3031.
   Google Scholar
• Bayardo, R., Jr., 1998. Efficiently mining long patterns from databases. ACM Sigmod Record, 27, 85–93. doi:10.1145/276305.276313
   Google Scholar
• Bayardo, R., Jr., Goethals, B., and Zaki, M., eds., 2004. FIMI 04, proceedings of the IEEE ICDM workshopon frequent itemset mining implementations. In: Proceedings of the IEEE ICDM workshop on frequent itemset mining implementations, 1 November, Brighton. Aachen: CEUR-WS.org, 126.
   Google Scholar
• Benkert, M., et al., 2008. Reporting flock patterns. Computational Geometry, 41, 111–125. doi:10.1016/j.comgeo.2007.10.003
   Web of Science ®Google Scholar
• Borgelt, C., 2005. An implementation of the FP-growth algorithm. In: Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations, 21–24 August, Chicago, IL. New York: ACM, 5.
   Google Scholar
• Brinkhoff, T., 2002. A framework for generating network-based moving objects. GeoInformatica, 6, 153–180. doi:10.1023/A:1015231126594
   Web of Science ®Google Scholar
• Brinkhoff, T., 2011. Network-based generator of moving objects [online]. Available from: http://iapg.jade-hs.de/personen/brinkhoff/generator/faq.php [Accessed 26 February 2014].
   Google Scholar
• Chavas, D.R. and Emanuel, K.A., 2010. A QuikSCAT climatology of tropical cyclone size. Geophysical Research Letters, 37. doi:10.1029/2010GL044558
   Web of Science ®Google Scholar
• Chen, R., et al., 2001. Mining association rules in analysis of transcription factors essential to gene expressions. In: Proceedings of the Atlantic symposium on computational biology, and genome information systems & technology, 15–17 March, Durham, NC.
   Google Scholar
• Creighton, C. and Hanash, S., 2003. Mining gene expression databases for association rules. Bioinformatics, 19, 79–86. doi:10.1093/bioinformatics/19.1.79
   PubMed Web of Science ®Google Scholar
• Dettki, H., Ericsson, G., and Edenius, L., 2004. Real-time moose tracking: an internet based mapping application using GPS/GSM-collars in Sweden. Alces, 40, 13–21.
   Google Scholar
• Folino, G. and Spezzano, G., 2002. An adaptive flocking algorithm for spatial clustering. In: J.J. Merelo Guervós, et al., eds. Parallel problem solving from nature PPSN VII, 7–11 September, Granada. Springer, 924–933. ISBN:978-3-540-45712-1 (Online).
   Google Scholar
• Frank, A., Raper, J., and Cheylan, J., 2001. Life and motion of socio-economic units. London: Taylor & Francis.
   Google Scholar
• Giudici, P. and Corporation, E., 2003. Applied data mining: statistical methods for business and industry. New York: Wiley.
   Google Scholar
• Goethals, B., 2003. Survey on frequent pattern mining. Manuscript, 1–43. Available from: http://win.ua.ac.be/~adrem/bibrem/pubs/fpm_survey.pdf [Accessed 21 February 2014].
   Google Scholar
• Goethals, B., 2004. The Fimi’04 homepage. Available from: http://fimi.cs.helsinki.fi/ [Accessed 21 February 2014].
   Google Scholar
• Goethals, B. and Zaki, M., eds., 2003. FIMI 03. Proceedings of the ICDM 2003 workshop on frequent itemset mining implementations, 19 December, Melbourne, FL, 90. CEUR Workshop Proceedings.
   Google Scholar
• Goethals, B. and Zaki, M., 2004. Advances in frequent itemset mining implementations: report on FIMI’03. ACM SIGKDD Explorations Newsletter, 6, 109–117. doi:10.1145/1007730.1007744
   Google Scholar
• Grahne, G. and Zhu, J., 2003. High performance mining of maximal frequent itemsets. In: Proceedings of the 6th international workshop on high performance data mining, May, San Francisco, CA.
   Google Scholar
• Gudmundsson, J. and van Kreveld, M., 2006. Computing longest duration flocks in trajectory data. In: S. Nittel, ed. Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems, 10–11 November, Arlington, VA. New York: ACM, 42.
   Google Scholar
• Gudmundsson, J., Van Kreveld, M., and 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, 12–13 November, Arlington, VA. New York: ACM, 250–257.
   Google Scholar
• Han, J., et al., 2007. Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery, 15, 55–86. doi:10.1007/s10618-006-0059-1
   Web of Science ®Google Scholar
• Han, J. and Kamber, M., 2006. Data mining: concepts and techniques. Waltham, MA: Morgan Kaufmann.
   Google Scholar
• Han, J., Koperski, K., and Stefanovic, N., 1997. GeoMiner: a system prototype for spatial data mining. In: J. Peckham, ed. Proceedings of the 1997 ACM SIGMOD international conference on Management of data, 13–15 May, Tucson, AZ. ACM Press, 553–556.
   Google Scholar
• Han, J. and Pei, J., 2000. Mining frequent patterns by pattern-growth: methodology and implications. ACM SIGKDD Explorations Newsletter, 2, 14–20. doi:10.1145/380995.381002
   Google Scholar
• Han, J., et al., 2004. Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Mining and Knowledge Discovery, 8, 53–87. doi:10.1023/B:DAMI.0000005258.31418.83
   Web of Science ®Google Scholar
• Iwase, S. and Saito, H., 2002. Tracking soccer player using multiple views. In: Proceedings of the IAPR workshop on machine vision applications (MVA02), 11–13 December, Nara, 102–105. ISBN:4-901122-02-9.
   Google Scholar
• Jensen, C., Lin, D., and Ooi, B.-C, eds., 2007. Continuous clustering of moving objects. IEEE Transactions on Knowledge and Data Engineering, 19 (9), 1161–1174. doi:10.1109/TKDE.2007.1054
   Web of Science ®Google Scholar
• Jeung, H., et al., 2008. Discovery of convoys in trajectory databases. Proceedings of the VLDB Endowment, 1, 1068–1080.
   Google Scholar
• Kalnis, P., Mamoulis, N., and Bakiras, S., 2005. On discovering moving clusters in spatio-temporal data. In: C.B. Medeiros, M.J. Egenhofer, and E. Bertino, eds. Proceedings of the 9th international symposium on Advances in spatial and temporal databases, SSTD 2005, 22–24 August, Angra dos Reis. Springer, 364–381. ISBN:978-3-540-28127-6 (Print), 978-3-540-31904-7 (Online).
   Google Scholar
• Kraak, M. and Huisman, O., 2009. Beyond exploratory visualization of space time paths. In: H.J. Miller and J. Han, eds. Geographic data mining and knowledge discovery. 2nd ed. London: Taylor & Francis, 431–443.
   Google Scholar
• Kraak, M. and Koussoulakou, A., 2004. A visualization environment for the space – time cube. In: P. Fisher, ed., Proceedings of the SDH 2004: proceedings of the 11th international symposium on spatial data handling: advances in spatial data handling II, 23–25 August, Zurich. Berlin: Springer, 189–200.
   Google Scholar
• Krajzewicz, D., et al., 2002. SUMO (Simulation of Urban MObility). In: A. Al-Akaidi, ed. Proceedings of the 4th Middle East symposium on simulation and modelling, September, Sharjah. Erlangen: SCS European Publishing House, 183–187.
   Google Scholar
• Laube, P., Kreveld, M., and Imfeld, S., 2005. Finding REMO – detecting relative motion patterns in geospatial lifelines. In: P.F. Fisher, ed. Developments in spatial data handling. Berlin: Springer-Verlag, 201–215.
   Google Scholar
• Makris, D. and Ellis, T., 2002. Path detection in video surveillance. Image and Vision Computing, 20, 895–903. doi:10.1016/S0262-8856(02)00098-7
   Web of Science ®Google Scholar
• Microsoft Research Asia, 2011. GeoLife Gps Trajectories. Available from: http://research.microsoft.com/en-us/downloads/b16d359d-d164-469e-9fd4-daa38f2b2e13/ [Accessed 21 February 2014].
   Google Scholar
• Mid 2002 Project, 2011. Mobility in Germany 2002. Available from: http://daten.clearingstelle-verkehr.de/196/ [Accessed 21 February 2014].
   Google Scholar
• Miller, H. and Han, J., 2001. Geographic data mining and knowledge discovery, Vol. 338. Boca Raton, FL: Wiley Online Library.
   Google Scholar
• Minato, S., Uno, T., and Arimura, H., 2008. LCM over ZBDDS: fast generation of very large-scale frequent itemsets using a compact graph-based representation. In: T. Washio, et al., eds. Advances in knowledge discovery and data mining, Berlin: Springer-Verlag, 234–246.
   Google Scholar
• National Climatic Data Center (NCDC), NOAA, 2013. International Best Track Archive for Climate Stewardship (IBTrACS). Available from: http://www.ncdc.noaa.gov/oa/ibtracs/index.php?name=ibtracs-data-access [Accessed 21 February 2014].
   Google Scholar
• Pan, F., et al., 2003. CARPENTER: finding closed patterns in long biological datasets. In: L. Getoor, et al., eds. Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, 24–27 August, Washington, DC. New York: ACM, 637–642.
   Google Scholar
• Pan, F., et al., 2004. COBBLER: combining column and row enumeration for closed pattern discovery. In: Proceedings of the scientific and statistical database management, 2004. 16th international conference, 21–23 June, Santorini Island. Washington, DC: IEEE Computer Society, 21–30. ISBN:0-7695-2146-0.
   Google Scholar
• Pasquier, N., et al., 1999. Discovering frequent closed itemsets for association rules. In: C. Beeri and P. Buneman, eds. Database Theory – ICDT’99, Lecture Notes in Computer Science 1540. Berlin: Springer, 398–416. Available from: http://link.springer.com/chapter/10.1007/3-540-49257-7_25
   Google Scholar
• Piciarelli, C., Foresti, G., and Snidara, L., 2006. Trajectory clustering and its applications for video surveillance. In: Proceedings of the advanced video and signal based surveillance, 2005. AVSS 2005. IEEE conference, 15–16 September, Como. Washington, DC: IEEE Computer Society, 40–45.
   Google Scholar
• Shan, M. and Wei, L., 2010. Algorithms for discovery of spatial co-orientation patterns from images. Expert Systems with Applications, 37, 5795–5802. doi:10.1016/j.eswa.2010.02.028
   PubMed Web of Science ®Google Scholar
• Sumo Project, 2011. TAPAS Cologne scenario. Available from: http://sourceforge.net/apps/mediawiki/sumo/index.php?title=Data/Scenarios/TAPASCologne [Accessed 21 February 2014].
   Google Scholar
• Uno, T., Kiyomi, M., and Arimura, H., 2004. LCM ver. 2: efficient mining algorithms for frequent/closed/maximal itemsets. In: Proceedings of the IEEE ICDM04 workshop FIMI04 (International conference on data mining, frequent itemset mining implementations), 1 November, Brighton.
   Google Scholar
• Uno, T., Kiyomi, M., and Arimura, H., 2005. Lcm ver. 3: collaboration of array, bitmap and prefix tree for frequent itemset mining. In: Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations, 21–24 August, Chicago, IL. New York: ACM, 77–86.
   Google Scholar
• Varschen, C. and Wagner, P. 2006, Mikroskopische Modellierung der Personenverkehrsnachfrage auf Basis von Zeitverwendungstagebüchern. Vortrag auf dem, 7.
   Google Scholar
• Vieira, M., Bakalov, P., and Tsotras, V. 2009, On-line discovery of flock patterns in spatio-temporal data. In: D. Agrawa, et al., eds. Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, 4–6 November, Seattle, WA. New York: ACM, 286–295.
   Google Scholar
• Wachowicz, M., et al., 2011. Finding moving flock patterns among pedestrians through collective coherence. International Journal of Geographical Information Science, 25, 1849–1864. doi:10.1080/13658816.2011.561209
   PubMed Web of Science ®Google Scholar
• Wood, Z. and Galton, A., 2009. A taxonomy of collective phenomena. Applied Ontology, 4, 267–292.
   Web of Science ®Google Scholar
• Yang, Y., Zhang, J., and Yang, J., 2008. Grid-based hierarchical spatial clustering algorithm in presence of obstacle and constraints. In: J. Ni, et al., eds. Proceedings of the 2008 international conference on internet computing in science and engineering, 28–29 January, Harbin. Seattle, WA: IEEE Computer Society, 383–388.
   Google Scholar
• Zhang, C. and Zhang, S., 2002. Association rule mining: models and algorithms. Berlin: Springer-Verlag.
   Google Scholar