Integration of profile hidden Markov model output into association rule mining
Christopher A. Besemann, Anne M Denton · 2005
Scientific models typically depend on parameters. Preserving the parameter dependence of models in the pattern mining context opens up several applications. Within association rule mining (ARM), the choice of parameters can be studied with more flexibly then in traditional model building. Studying support, confidence, and other rule metrics as a function of model parameters allows conclusions on assumptions underlying the models. We present efficient techniques to handle multiple model output data sets at little more than the cost of one. We integrate output from hidden Markov models into the association rule mining framework, demonstrating the potential for frequent pattern mining in the field of scientific modeling and experimentation.