Abstract
The research tracks the spread of co-occurrence phenomena over the zonal space. Spread patterns of spatio-temporal co-occurrences over zones (SPCOZs) represent the spread structures over the zones for the subsets of features whose events co-locate in space and time. SPCOZs are of great use in many applications, such as tracking the evolutions of infectious diseases and ecological disasters in space and time. However, finding SPCOZs is computationally expensive due to large size of history data sets, exponential number of feature combinations, and complex interest measures. In this paper, we propose a novel Spread Pattern Tree (SP-Tree) to index the spread elements of the SPCOZs which holds the monotonic property with the size of the co-occurrences. We also propose an efficient mining algorithm (SPCOZ-Miner) for mining SPCOZs. The experimental evaluation with both synthetic and real-world data sets shows our algorithm is effective and much more efficient than a straight approach.
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References
Huang, Y., Shekhar, S., Xiong, H.: Discovering colocation patterns from spatial datasets: A general approach. IEEE Transactions on Knowledge and Data Engineering 16(12), 1472–1485 (2004)
Yoo, J., Shekhar, S., Smith, J., Kumquat, J.: A partial join approach for mining co-location patterns. In: Proceedings of the 12th annual ACM international workshop on geographic information systems, pp. 241–249 (2004)
Yoo, J., Shekhar, S.: A Joinless Approach for Mining Spatial Colocation Patterns. IEEE Transactions on Knowledge and Data Engineering 18(10), 1323–1337 (2006)
Sheng, C., Hsu, W., Li Lee, M., Tung, A.: Discovering Spatial Interaction Patterns. In: Haritsa, J.R., Kotagiri, R., Pudi, V. (eds.) DASFAA 2008. LNCS, vol. 4947, pp. 95–109. Springer, Heidelberg (2008)
Huang, Y., Pei, J., Xiong, H.: Mining Co-Location Patterns with Rare Events from Spatial Data Sets. GeoInformatica 10(3), 239–260 (2006)
Celik, M., Shekhar, S., Rogers, J., Shine, J., Yoo, J.: Mixed-drove spatio-temporal co-occurrence pattern mining: A summary of results. In: Proceedings of the 6th international conference on data mining, pp. 119–128 (2006)
Celik, M., Shekhar, S., Rogers, J., Shine, J.: Sustained Emerging Spatio-Temporal Co-occurrence Pattern Mining: A Summary of Results. In: Proceedings of the 18th IEEE international conference on tools with artificial intelligence, pp. 106–115 (2006)
Celik, M., Shekhar, S., Rogers, J., Shine, J., Kang, J.: Mining At Most Top-K Mixed-drove Spatio-temporal Co-occurrence Patterns: A Summary of Results. In: Proceedings of the 23rd IEEE international conference on data engineering workshop, pp. 565–574 (2007)
He, J., He, Q., Qian, F., Chen, Q.: Incremental Maintenance of Discovered Spatial Colocation Patterns. In: Proceedings of the 8th international conference on data mining workshop on spatial and spatio-temporal data mining, pp. 399–407 (2008)
An Arbitrary Timeline of History, http://www.karisteeves.net/teach/timeline.htm
Disaster Information: Hurricane season, http://www.giis.org/consumer/disaster.shtml
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th international conference on very large data bases, vol. 1215, pp. 487–499 (1994)
Celik, M., Kang, J., Shekhar, S.: Zonal Co-location Pattern Discovery with Dynamic Parameters. In: Proceddings of the 7th IEEE international conference on data mining, pp. 433–438 (2007)
Eick, C., Parmar, R., Ding, W., Stepinski, T., Nicot, J.: Finding regional co-location patterns for sets of continuous variables in spatial datasets. In: Proceedings of the 16th ACM SIGSPATIAL international conference on advances in geographic information systems (2008)
Kalnis, P., Mamoulis, N., Bakiras, S.: On Discovering Moving Clusters in Spatio-temporal Data. In: Proceedings of the 9th international symposium on advances in spatial and temporal databases, pp. 364–381 (2005)
Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D.: Trajectory pattern mining. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 330–339 (2007)
Mamoulis, N., Cao, H., Kollios, G., Hadjieleftheriou, M., Tao, Y., Cheung, D.: Mining, indexing, and querying historical spatiotemporal data. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, pp. 236–245 (2004)
Giannotti, F., Nanni, M., Pedreschi, D.: Efficient mining of temporally annotated sequences. In: Proceedings of the 6th SIAM international conference on data mining, pp. 346–357 (2006)
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Qian, F., He, Q., He, J. (2009). Mining Spread Patterns of Spatio-temporal Co-occurrences over Zones. In: Gervasi, O., Taniar, D., Murgante, B., Laganà, A., Mun, Y., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2009. ICCSA 2009. Lecture Notes in Computer Science, vol 5593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02457-3_57
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DOI: https://doi.org/10.1007/978-3-642-02457-3_57
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