Title :
New Approach on Temporal Data Mining for Symbolic Time Sequences: Temporal Tree Associate Rules
Author :
Guillame-Bert, Mathieu ; Crowley, James L.
Author_Institution :
INRIA Rhone-Alpes Res. Center, Montbonnot-St.-Martin, France
Abstract :
We introduce a temporal pattern model called Temporal Tree Associative Rule (TTA rule). This pattern model can be used to express both uncertainty and temporal inaccuracy of temporal events expressed as Symbolic Time Sequences. Among other things, TTA rules can express the usual time point operators, synchronicity, order, chaining, as well as temporal negation. TTA rule is designed to allows predictions with optimum temporal precision. Using this representation, we present an algorithm that can be used to extract Temporal Tree Associative rules from large data sets of symbolic time sequences. This algorithm is a mining heuristic based on entropy maximisation and statistical independence analysis. We discuss the evaluation of probabilistic temporal rules, evaluate our technique with an experiment and discuss the results.
Keywords :
data mining; entropy; statistical analysis; TTA rule; entropy maximisation; statistical independence analysis; symbolic time sequences; temporal data mining; temporal tree associate rules; Algorithm design and analysis; Data mining; Heuristic algorithms; Histograms; Prediction algorithms; Sensors; Time series analysis; augmented environments; data mining; knowledge discovery; temporal patterns;
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on
Conference_Location :
Boca Raton, FL
Print_ISBN :
978-1-4577-2068-0
Electronic_ISBN :
1082-3409
DOI :
10.1109/ICTAI.2011.117
