This paper focuses on using data mining technology to efficiently and accurately discover habitats and stopovers of migratory birds. The three methods we used are as follows: 1. a density-based clustering method, detecting stopovers of birds during their migration through density-based clustering of location points; 2. A location histories parser method, detecting areas that have been overstayed by migratory birds during a set time period by setting time and distance thresholds; and 3. A time-parameterized line segment clustering method, clustering directed line segments to analyze shared segments of migratory pathways of different migratory birds and discover the habitats and stopovers of these birds. Finally, we analyzed the migration data of the bar-headed goose in the Qinghai Lake Area through the three above methods and verified the effectiveness of the three methods and, by comparison, identified the scope and context of the use of these three methods respectively.
How to Cite:
Xu, Q. et al. , (2013). Data Mining Approaches for Habitats and Stopovers Discovery of Migratory Birds . Data Science Journal . 12 , pp . WDS159–WDS169 . DOI: http://doi.org/10.2481/dsj.WDS-027