June 15, 2014
June 15, 2014
June 18, 2014
Computing & Information Technology
24.1079.1 - 24.1079.8
Sequence Data Mining for Adverse Event Prediction and Action RecommendationMany real-life data mining applications uses sequence data modeling in which data isrepresented as a sequence. A sequence is an ordered list of events (t1,e1), (t2,e2), …,(tn,en)where ti represents time and ei represents the event taking place at time ti. ei takes placebefore ei+1 for 1≤ i ≤ n-1. This model can be used in data mining, called sequence datamining, to predict certain event that may take place at a specific time.Sequence data mining has a wide range of applications in the data mining field. This datamining technique can be used for prediction of adverse events and recommend properactions to be taken as needed. For the aviation safety, the future of a flight can bepredicted as a sequence and proper action can be recommended to avoid dangeroussituations that a flight may get into otherwise. In the health care system, the future of abacterial infection can be predicted and proper medicine can be prescribed for differentsituations to bring the patient’s illness to an end. In the marketing, customer sopping canbe monitored and certain action can be taken, such as mailing coupons, to encourage thecustomer for further sopping of relevant products. In the real-life situations such asmanufacturing plants, sensors’ data can be analyzed to control operations and predictdangerous situations and recommend proper actions. This paper discusses a new technique for implementation of sequence data mining andits applications for a number of different cases.
Sanati-Mehrizy, R., & Minaie, A., & Kuddus, R. H., & Sanati-Mehrizy, A., & Sanati-Mehrizy, P. (2014, June), Sequence Data Mining for Adverse Event Prediction and Action Recommendation Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. https://peer.asee.org/23012
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