Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Discovery of Frequent Episodes in Event Sequences
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
Efficient Mining of Constrained Correlated Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Mining Railway Delay Dependencies in Large-Scale Real-World Delay Data
Robust and Online Large-Scale Optimization
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Valency based weighted association rule mining
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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In order to meet the mounting social and economic demands, railway operators and manufacturers are striving for a longer availability and a better reliability of railway transportation systems. Commercial trains are being equipped with state-of-the-art onboard intelligent sensors monitoring various subsystems all over the train. These sensors provide real-time spatio-temporal data consisting of georeferenced timestamped events that tend sometimes to occur in bursts. Once ordered with respect to time, these events can be considered as long temporal sequences that can be mined for possible relationships leading to association rules. In this paper, we propose a methodology for discovering association rules in very bursty and challenging floating train data sequences with multiple constraints. This methodology is based on using null models to discover significant co-occurrences between pairs of events. Once identified and scrutinized by various metrics, these co-occurrences are then used to derive temporal association rules that can predict the imminent arrival of severe failures. Experiments performed on Alstom's TrainTracerTM data show encouraging results.