Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Hidden Markov Model-Based Approach to Sequential Data Clustering
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Local and Global Methods in Data Mining: Basic Techniques and Open Problems
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Pushing Convertible Constraints in Frequent Itemset Mining
Data Mining and Knowledge Discovery
Twain: Two-end association miner with precise frequent exhibition periods
ACM Transactions on Knowledge Discovery from Data (TKDD)
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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.