Abstract
Spatiotemporal co-occurrence rules (STCORs) discovery is an important problem in many application domains such as weather monitoring and solar physics, which is our application focus. In this paper, we present a general framework to identify STCORs for continuously evolving spatiotemporal events that have extended spatial representations. We also analyse a set of anti-monotone (monotonically non-increasing) and non anti-monotone measures to identify STCORs. We then validate and evaluate our framework on a real-life data set and report results of the comparison of the number candidates needed to discover actual patterns, memory usage, and the number of STCORs discovered using the anti-monotonic and non anti-monotonic measures.
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References
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207–216 (1993)
Cao, H., Mamoulis, N., Cheung, D.W.: Discovery of collocation episodes in spatiotemporal data. In: The 6th Intern. Conf. on Data Mining, DC, pp. 823–827 (2006)
Celik, M., Shekhar, S., Rogers, J.P., Shine, J.A., Yoo, J.S.: Mixed-drove spatio-temporal co-occurence pattern mining: A summary of results. In: The 6th Intern. Conf. on Data Mining, DC, pp. 119–128 (2006)
Egghe, L., Michel, C.: Strong similarity measures for ordered sets of documents in information retrieval. Inf. Process. Manag. 38(6), 823–848 (2002)
HEK (January 2012), http://www.lmsal.com/isolsearch
Huang, Y., Shekhar, S., Xiong, H.: Discovering colocation patterns from spatial data sets: a general approach. Trans. on Know. and Data Eng., 1472–1485 (2004)
Manning, C.D., Schütze, H.: Foundations of statistical natural language processing. MIT Press, Cambridge (1999)
Patel, D.: Interval-orientation patterns in spatio-temporal databases. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds.) DEXA 2010, Part I. LNCS, vol. 6261, pp. 416–431. Springer, Heidelberg (2010)
Pillai, K.G., Angryk, R.A., Banda, J.M., Schuh, M.A., Wylie, T.: Spatio-temporal co-occurrence pattern mining in data sets with evolving regions. In: ICDM Workshops, pp. 805–812 (2012)
Schuh, M.A., Angryk, R.A., Pillai, K.G., Banda, J.M., Martens, P.C.: A large-scale solar image dataset with labeled event regions. In: Int. Conf. on Image Processing, ICIP (2013)
Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (2005)
Taylor, P.: Quantitative Methods in Geography: An Introduction to Spatial Analysis. Houghton Mifflin (1977)
Wang, J., Hsu, W., Lee, M.L.: A framework for mining topological patterns in spatio-temporal databases. In: CIKM 2005, pp. 429–436. ACM, New York (2005)
Xiong, H., Shekhar, S., Huang, Y., Kumar, V., Ma, X., Yoo, J.S.: A framework for discovering co-location patterns in data sets with extended spatial objects. In: SDM (2004)
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Ganesan Pillai, K., Angryk, R.A., Banda, J.M., Wylie, T., Schuh, M.A. (2014). Spatiotemporal Co-occurrence Rules. In: Catania, B., et al. New Trends in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol 241. Springer, Cham. https://doi.org/10.1007/978-3-319-01863-8_3
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DOI: https://doi.org/10.1007/978-3-319-01863-8_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01862-1
Online ISBN: 978-3-319-01863-8
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