Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficient Data Mining for Maximal Frequent Subtrees
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGKDD International Conference on Knowledge Discovery and 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
Mining Frequent Spatio-Temporal Sequential Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Efficiently Mining Frequent Embedded Unordered Trees
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
DryadeParent, An Efficient and Robust Closed Attribute Tree Mining Algorithm
IEEE Transactions on Knowledge and Data Engineering
Mixed-Drove Spatiotemporal Co-Occurrence Pattern Mining
IEEE Transactions on Knowledge and Data Engineering
WhereNext: a location predictor on trajectory pattern mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Migration motif: a spatial - temporal pattern mining approach for financial markets
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Spread Patterns of Spatio-temporal Co-occurrences over Zones
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
Mining frequent closed rooted trees
Machine Learning
Frequent tree pattern mining: A survey
Intelligent Data Analysis
IMB3-Miner: mining induced/embedded subtrees by constraining the level of embedding
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Weighted path as a condensed pattern in a single attributed DAG
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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These last years an increasing amount of spatio-temporal data has been collected to study complex natural phenomena (e.g. natural hazards, environmental change, spread of infectious diseases). Extracting knowledge to better understand the dynamic of these phenomena is a challenging task. Existing works typically use patterns (e.g. sequences, trees, graphs) to model the dynamic of the phenomenon. However, the spatio-temporal properties captured by these patterns are often limited. For example, they hardly capture the spatial and temporal interactions of factors in different districts when studying the spread of a virus. In this paper, we define a new type of pattern, called complex spatio-temporal tree, to better capture the spatio-temporal properties of natural phenomena. Then, we show how a "classical" tree mining algorithm can be used to extract these complex spatio-temporal patterns. We experiment our approach on three datasets: synthetic data, real dengue data and real erosion data. The preliminary results highlighted the interest of our approach.