Abstract
Knowledge about the wetland use of migratory bird species during the annual life circle is very interesting to biologists, as it is critically important for conservation site construction and avian influenza control. The raw data of the habitat areas and the migration routes can be determined by high-tech GPS satellite telemetry, that usually are large scale with high complexity. In this paper, we convert these biological problems into computational studies, and introduce efficient algorithms for the data analysis. Our key idea is the concept of hierarchical clustering for migration habitat localization, and the notion of association rules for the discovery of migration routes. One of our clustering results is the Spatial-Tree, an illusive map which depicts the home range of bar-headed geese. A related result to this observation is an association pattern that reveals a high possibility of bar-headed geese’s potential migration routes. Both of them are of biological novelty and meaning.
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References
Liu, J., et al.: Highly pathogenic H5N1 influenza virus infection in migratory birds. Science 309, 1206 (2005)
Li, Z.W.D., Mundkur, T.: Numbers and distribution of waterbirds and wetlands in the Asia-Pacific region: results of the Asian Waterbird Census 2002–2004. Wetlands International, Kuala Lumpur (2007)
Worton, B.J.: kernel methods for estimating the utilization distribution in home-range studies. Ecology 70, 164–168 (1989)
Kanai, Y., et al.: Discovery of breeding grounds of a Siberian Crane Grus leucogeranus flock that winter sin Iran, via satellite telemetry. Bird Conservation International 12, 327–333 (2002)
Mathevet, R., Tamisier, A.: Creation of a nature reserve, its effects on hunting management and waterfowl distribution in the Camargue (southern France). Biodiv. Conserv. 11, 509–519 (2002)
Shimazaki, H., et al.: Migration routes and important stopover sites of endangered oriental white storks (Ciconia boyciana) as revealed by satellite tracking
Ball, G.H., Hall, D.J.: ISODATA: a novel method of data analysis and pattern classification. Technical Report of Stanford Research Institute, Menlo Park, CA, Stanford Research Institute, 66 (1965)
Ester, M., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, Portland, OR, pp. 226–231 (1996)
Ester, M., Kriegel, H., Sander, J., Xu, X.: Incremental Clustering for Mining in a Data Warehousing Environment VLDB (1998)
Ng, R.T., Han, J.: Efficient and Effective Clustering Methods for Spatial Data Mining. In: Proc. 20th Int. Conf. on Very Large Data Bases, Santiago, Chile, pp. 144–155 (1994)
Ertöz, L., Steinbach, M., Kumar, V.: Finding topics in collections of documents: A shared nearest neighbor approach. In: Proceedings of Text Mine 2001, First SIAM International Conference on Data Mining, Chicago, IL,USA (2001)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley and Sons, Inc., New York (1990)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proc. of the 20th VLDB Conference (1994)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. International Journal of Data Mining and Knowledge Discovery 8(1), 53–87 (2004)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the 11th International Conference on Data Engineering (ICDE 1995) Taipei, Taipei, Taiwan, pp. 3–14 (1995)
Zaki, M.J.: Efficient Enumeration of Frequent Sequences. In: 7th International Conference on Information and Knowledge Management, Washington DC, November 1998, pp. 68–75 (1998)
Koperski, K., Han, J.: Discovery of Spatial Association Rules in Geographic Information Databases. In: Egenhofer, M.J., Herring, J.R. (eds.) SSD 1995. LNCS, vol. 951, pp. 47–66. Springer, Heidelberg (1995)
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H.: Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of the 2001 International Conference on Data Engineering (ICDE 2001), pp. 214–224 (2001)
Muzaffar, S.B., Johny, T.: Seasonal movements and migration of Pallas’s Gulls Larus ichthyaetus from Qinghai Lake, China. Forktail 24, 100–107 (2008)
Miyabayashi, Y., Mundkur, T.: Atlas of key sites for Anatidae in the East Asian Flyway. Wetlands International—AsiaPacific, Tokyo: Japan, and Kuala Lumpur, Malaysia (1999), http://www.jawgp.org/anet/aaa1999/aaaendx.htm (accessed March 11, 2008)
Shan, G.H., JunYu, L., QiYing, L.: Introduction to ACM international Collegiate Programming Contest, 2nd edn., pp. 100–102 (in Chinese)
Weisstein, E.W.: Spherical Polygon. From MathWorld–A Wolfram Web Resource, http://mathworld.wolfram.com/SphericalPolygon.html
Daniel Sheldon, M.A.: Saleh Elmohamed, Dexter Kozen. In: Collective Inference on Markov Models for Model Appendix: Springer-Author Discount
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Tang, M. et al. (2009). Discovery of Migration Habitats and Routes of Wild Bird Species by Clustering and Association Analysis. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science(), vol 5678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03348-3_29
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DOI: https://doi.org/10.1007/978-3-642-03348-3_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03347-6
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