Many real-life data mining applications use sequence data modeling, in which data is represented as a sequence. A temporal sequence is a finite ordered list of events (t_1,e_1), (t_2,e_2), ...,(t_n,e_n) where ti represents time and e_i represents the event taking place at time t_i. e_i takes place before e_(i+1) for 1≤ i ≤ n-1. This model can be used in data mining, called sequence data mining, to predict certain event that may take place at a specific time. Sequence data mining has a wide range of applications. This data mining technique can be used for prediction of adverse events and can recommend appropriate actions to be taken as needed. In aviation safety, the future of a flight can be predicted as a sequence and proper action can be recommended to avoid dangerous situations that a flight may get into otherwise. In health care, the future of a bacterial infection can be predicted and proper medicine can be prescribed for different situations to bring the patient's illness to an end. In marketing, customer shopping can be monitored and certain actions can be taken, such as mailing coupons, to encourage customers to engage in repeat shopping. In manufacturing, sensor data can be analyzed to regulate operations and predict and avoid dangerous situations by recommending appropriate actions. This paper which is the continuation of the work by Sanati et. al.1, discusses sequence representation, implementation, and its application for a number of different fileds.
展开▼
