document: Integration of profile hidden Markov model output into association rule mining 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 .
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