ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
A Polynomial Algorithm for Submap Isomorphism
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
What is frequent in a single graph?
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Frequent subgraph mining in outerplanar graphs
Data Mining and Knowledge Discovery
New application of graph mining to video analysis
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
CPM'11 Proceedings of the 22nd annual conference on Combinatorial pattern matching
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Dynamic graph mining is the task of searching for subgraph patterns that capture the evolution of a dynamic graph. In this paper, we are interested in mining dynamic graphs in videos. A video can be regarded as a dynamic graph, whose evolution over time is represented by a series of plane graphs, one graph for each video frame. As such, subgraph patterns in this series may correspond to objects that frequently appear in the video. Furthermore, by associating spatial information to each of the nodes in these graphs, it becomes possible to track a given object through the video in question. We present, in this paper, two plane graph mining algorithms, called plagram and dyplagram, for the extraction of spatiotemporal patterns. A spatiotemporal pattern is a set of occurrences of a given subgraph pattern which are not too far apart w.r.t time nor space. Experiments demonstrate that our algorithms are effective even in contexts where general-purpose algorithms would not provide the complete set of frequent subgraphs. We also show that they give promising results when applied to object tracking in videos.