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- research-articleAugust 2012
Sampling minimal frequent boolean (DNF) patterns
KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2012, Pages 87–95https://doi.org/10.1145/2339530.2339547We tackle the challenging problem of mining the simplest Boolean patterns from categorical datasets. Instead of complete enumeration, which is typically infeasible for this class of patterns, we develop effective sampling methods to extract a ...
- research-articleAugust 2011
Direct local pattern sampling by efficient two-step random procedures
KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2011, Pages 582–590https://doi.org/10.1145/2020408.2020500We present several exact and highly scalable local pattern sampling algorithms. They can be used as an alternative to exhaustive local pattern discovery methods (e.g, frequent set mining or optimistic-estimator-based subgroup discovery) and can ...
- ArticleOctober 2010
Mining class-correlated patterns for sequence labeling
DS'10: Proceedings of the 13th international conference on Discovery scienceOctober 2010, Pages 311–325Sequence 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 ...
- ArticleSeptember 2010
NDPMine: efficiently mining discriminative numerical features for pattern-based classification
ECML PKDD'10: Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part IISeptember 2010, Pages 35–50Pattern-based classification has demonstrated its power in recent studies, but because the cost of mining discriminative patterns as features in classification is very expensive, several efficient algorithms have been proposed to rectify this problem. ...
- ArticleSeptember 2010
NDPMine: efficiently mining discriminative numerical features for pattern-based classification
ECMLPKDD'10: Proceedings of the 2010th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part IISeptember 2010, Pages 35–50https://doi.org/10.1007/978-3-642-15883-4_3Pattern-based classification has demonstrated its power in recent studies, but because the cost of mining discriminative patterns as features in classification is very expensive, several efficient algorithms have been proposed to rectify this problem. ...
- research-articleJune 2009
Classification of software behaviors for failure detection: a discriminative pattern mining approach
KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data miningJune 2009, Pages 557–566https://doi.org/10.1145/1557019.1557083Software is a ubiquitous component of our daily life. We often depend on the correct working of software systems. Due to the difficulty and complexity of software systems, bugs and anomalies are prevalent. Bugs have caused billions of dollars loss, in ...
- ArticleDecember 2008
Classifying High-Dimensional Text and Web Data Using Very Short Patterns
ICDM '08: Proceedings of the 2008 Eighth IEEE International Conference on Data MiningDecember 2008, Pages 923–928https://doi.org/10.1109/ICDM.2008.139In this paper, we propose the "Democratic Classifier", a simple pattern-based classification algorithm that uses very short patterns for classification, and does not rely on the minimum support threshold. Borrowing ideas from democracy, our training ...