Molecular biology for computer scientists
Artificial intelligence and molecular biology
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Matrix analysis and applied linear algebra
Matrix analysis and applied linear algebra
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Managing Interesting Rules in Sequence Mining
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Pattern Detection and Discovery
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Equivalence and Reduction of Hidden Markov Models
Equivalence and Reduction of Hidden Markov Models
Exploitation of Unlabeled Sequences in Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interestingness of frequent itemsets using Bayesian networks as background knowledge
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast discovery of unexpected patterns in data, relative to a Bayesian network
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Discovering Frequent Episodes and Learning Hidden Markov Models: A Formal Connection
IEEE Transactions on Knowledge and Data Engineering
Protein Fold Recognition using a Structural Hidden Markov Model
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Contrasting the Contrast Sets: An Alternative Approach
IDEAS '07 Proceedings of the 11th International Database Engineering and Applications Symposium
Scalable pattern mining with Bayesian networks as background knowledge
Data Mining and Knowledge Discovery
Semisupervised Learning of Hidden Markov Models via a Homotopy Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Guest Editorial: Global modeling using local patterns
Data Mining and Knowledge Discovery
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The paper presents a method of interactive construction of global Hidden Markov Models (HMMs) based on local sequence patterns discovered in data. The method is based on finding interesting sequences whose frequency in the database differs from that predicted by the model. The patterns are then presented to the user who updates the model using their intelligence and their understanding of the modelled domain. It is demonstrated that such an approach leads to more understandable models than automated approaches. Two variants of the problem are considered: mining patterns occurring only at the beginning of sequences and mining patterns occurring at any position; both practically meaningful. For each variant, algorithms have been developed allowing for efficient discovery of all sequences with given minimum interestingness. Applications to modelling webpage visitors behavior and to modelling protein secondary structure are presented, validating the proposed approach.