HASAR : mining sequential association rules for atherosclerosis risk factor analysis

Abstract : We present the HASAR method that is an hybrid approach for ex- tracting adaptive sequential association rules. This method extracts association rules between events occurring in subsequent time-intervals using closed itemsets extraction and evolutionary techniques. An important feature is its capacity to consider different time-intervals depending on the attributes semantic. We applied this method for the analysis of long term medical observations of atherosclerosis risk factors for cardio-vascular diseases prevention. Experimental results show that it is well-suited for extracting knowledge from temporal data where interesting patterns have different observation period length.
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https://hal.archives-ouvertes.fr/hal-02341338
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  • HAL Id : hal-02341338, version 1

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Laurent Brisson, Nicolas Pasquier, Céline Hebert, Martine Collard. HASAR : mining sequential association rules for atherosclerosis risk factor analysis. Workshop in 8th PKDD conference (Principles and Practice of Knowledge Discovery in Databases), Sep 2004, Pisa, Italy. ⟨hal-02341338⟩

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