Detecting change in categorical data: mining contrast sets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Transversing itemset lattices with statistical metric pruning
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
A Hidden Markov Model for Predicting Transmembrane Helices in Protein Sequences
ISMB '98 Proceedings of the 6th International Conference on Intelligent Systems for Molecular Biology
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Practical Algorithm to Find the Best Subsequence Patterns
DS '00 Proceedings of the Third International Conference on Discovery Science
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Space Efficient String Mining under Frequency Constraints
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Correlated itemset mining in ROC space: a constraint programming approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-class correlated pattern mining
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
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Sequence labeling is the task of assigning a label sequence to an observation sequence. Since many methods to solve this problem depend on the specification of predictive features, automated methods for their derivation are desirable. Unlike in other areas of pattern-based classification, however, no algorithm to directly mine class-correlated patterns for sequence labeling has been proposed so far. We introduce the novel task of mining class-correlated sequence patterns for sequence labeling and present a supervised pattern growth algorithm to find all patterns in a set of observation sequences, which correlate with the assignment of a fixed sequence label no less than a user-specified minimum correlation constraint. From the resulting set of patterns, features for a variety of classifiers can be obtained in a straightforward manner. The efficiency of the approach and the influence of important parameters are shown in experiments on several biological datasets.