Learning Temporal Association Rules on Symbolic Time Sequences

Mathieu Guillame-Bert, James L. Crowley
Proceedings of the Asian Conference on Machine Learning, PMLR 25:159-174, 2012.

Abstract

We introduce a temporal pattern model called Temporal Interval Tree Association Rules (Tita rules or Titar). This pattern model can express both uncertainty and temporal inaccuracy of temporal events. Among other things, Tita rules can express the usual time point operators, synchronicity, order, and chaining, as well as temporal negation and disjunctive temporal constraints. Using this representation, we present the Titar learner algorithm that can be used to extract Tita rules from large datasets expressed as Symbolic Time Sequences. The selection of temporal constraints (or time-frames) is at the core of the temporal learning. Our learning algorithm is based on two novel approaches for this problem. This first one is designed to select temporal constraints for the head of temporal association rules. The second selects temporal constraints for the body of such rules. We discuss the evaluation of probabilistic temporal association rules, evaluate our technique with two experiments, introduce a metric to evaluate sets of temporal rules, compare the results with two other approaches and discuss the results.

Cite this Paper

BibTeX
@InProceedings{pmlr-v25-guillame-bert12, title = {Learning Temporal Association Rules on Symbolic Time Sequences}, author = {Guillame-Bert, Mathieu and Crowley, James L.}, booktitle = {Proceedings of the Asian Conference on Machine Learning}, pages = {159--174}, year = {2012}, editor = {Hoi, Steven C. H. and Buntine, Wray}, volume = {25}, series = {Proceedings of Machine Learning Research}, address = {Singapore Management University, Singapore}, month = {04--06 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v25/guillame-bert12/guillame-bert12.pdf}, url = {https://proceedings.mlr.press/v25/guillame-bert12.html}, abstract = {We introduce a temporal pattern model called Temporal Interval Tree Association Rules (Tita rules or Titar). This pattern model can express both uncertainty and temporal inaccuracy of temporal events. Among other things, Tita rules can express the usual time point operators, synchronicity, order, and chaining, as well as temporal negation and disjunctive temporal constraints. Using this representation, we present the Titar learner algorithm that can be used to extract Tita rules from large datasets expressed as Symbolic Time Sequences. The selection of temporal constraints (or time-frames) is at the core of the temporal learning. Our learning algorithm is based on two novel approaches for this problem. This first one is designed to select temporal constraints for the head of temporal association rules. The second selects temporal constraints for the body of such rules. We discuss the evaluation of probabilistic temporal association rules, evaluate our technique with two experiments, introduce a metric to evaluate sets of temporal rules, compare the results with two other approaches and discuss the results.} }
Endnote
%0 Conference Paper %T Learning Temporal Association Rules on Symbolic Time Sequences %A Mathieu Guillame-Bert %A James L. Crowley %B Proceedings of the Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2012 %E Steven C. H. Hoi %E Wray Buntine %F pmlr-v25-guillame-bert12 %I PMLR %P 159--174 %U https://proceedings.mlr.press/v25/guillame-bert12.html %V 25 %X We introduce a temporal pattern model called Temporal Interval Tree Association Rules (Tita rules or Titar). This pattern model can express both uncertainty and temporal inaccuracy of temporal events. Among other things, Tita rules can express the usual time point operators, synchronicity, order, and chaining, as well as temporal negation and disjunctive temporal constraints. Using this representation, we present the Titar learner algorithm that can be used to extract Tita rules from large datasets expressed as Symbolic Time Sequences. The selection of temporal constraints (or time-frames) is at the core of the temporal learning. Our learning algorithm is based on two novel approaches for this problem. This first one is designed to select temporal constraints for the head of temporal association rules. The second selects temporal constraints for the body of such rules. We discuss the evaluation of probabilistic temporal association rules, evaluate our technique with two experiments, introduce a metric to evaluate sets of temporal rules, compare the results with two other approaches and discuss the results.
RIS
TY - CPAPER TI - Learning Temporal Association Rules on Symbolic Time Sequences AU - Mathieu Guillame-Bert AU - James L. Crowley BT - Proceedings of the Asian Conference on Machine Learning DA - 2012/11/17 ED - Steven C. H. Hoi ED - Wray Buntine ID - pmlr-v25-guillame-bert12 PB - PMLR DP - Proceedings of Machine Learning Research VL - 25 SP - 159 EP - 174 L1 - http://proceedings.mlr.press/v25/guillame-bert12/guillame-bert12.pdf UR - https://proceedings.mlr.press/v25/guillame-bert12.html AB - We introduce a temporal pattern model called Temporal Interval Tree Association Rules (Tita rules or Titar). This pattern model can express both uncertainty and temporal inaccuracy of temporal events. Among other things, Tita rules can express the usual time point operators, synchronicity, order, and chaining, as well as temporal negation and disjunctive temporal constraints. Using this representation, we present the Titar learner algorithm that can be used to extract Tita rules from large datasets expressed as Symbolic Time Sequences. The selection of temporal constraints (or time-frames) is at the core of the temporal learning. Our learning algorithm is based on two novel approaches for this problem. This first one is designed to select temporal constraints for the head of temporal association rules. The second selects temporal constraints for the body of such rules. We discuss the evaluation of probabilistic temporal association rules, evaluate our technique with two experiments, introduce a metric to evaluate sets of temporal rules, compare the results with two other approaches and discuss the results. ER -
APA
Guillame-Bert, M. & Crowley, J.L.. (2012). Learning Temporal Association Rules on Symbolic Time Sequences. Proceedings of the Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 25:159-174 Available from https://proceedings.mlr.press/v25/guillame-bert12.html.

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