Abstract
Recent advances in satellite tracking technologies can provide huge amount of data for biologists to understand continuous long movement patterns of wild bird species. In particular, highly correlated habitat areas are of great biological interests. Biologists can use this information to strive potential ways for controlling highly pathogenic avian influenza. We convert these biological problems into graph mining problems. Traditional models for frequent graph mining assign each vertex label with equal weight. However, the weight difference between vertexes can make strong impact on decision making by biologists. In this paper, by considering different weights of individual vertex in the graph, we develop a new algorithm, Helen, which focuses on identifying cliques with high weights. We introduce “graph-weighted support framework” to reduce clique candidates, and then filter out the low weighted cliques. We evaluate our algorithm on real life birds’ migration data sets, and show that graph mining can be very helpful for ecologists to discover unanticipated bird migration relationships.
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Tang, M. et al. (2010). Birds Bring Flues? Mining Frequent and High Weighted Cliques from Birds Migration Networks. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds) Database Systems for Advanced Applications. DASFAA 2010. Lecture Notes in Computer Science, vol 5982. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12098-5_30
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DOI: https://doi.org/10.1007/978-3-642-12098-5_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12097-8
Online ISBN: 978-3-642-12098-5
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