Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Cross-Graph Quasi-Cliques in Gene Expression and Protein Interaction Data
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
CLAN: An Algorithm for Mining Closed Cliques from Large Dense Graph Databases
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Coherent closed quasi-clique discovery from large dense graph databases
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
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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.