C4.5: programs for machine learning
C4.5: programs for machine learning
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Feature Construction with Version Spaces for Biochemical Applications
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
On the Complexity of Generating Maximal Frequent and Minimal Infrequent Sets
STACS '02 Proceedings of the 19th Annual Symposium on Theoretical Aspects of Computer Science
DualMiner: A Dual-Pruning Algorithm for Itemsets with Constraints
Data Mining and Knowledge Discovery
Using transposition for pattern discovery from microarray data
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Finding the most interesting patterns in a database quickly by using sequential sampling
The Journal of Machine Learning Research
ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraints
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Carpenter: finding closed patterns in long biological datasets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
DeEPs: A New Instance-Based Lazy Discovery and Classification System
Machine Learning
On Closed Constrained Frequent Pattern Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Constraint-based concept mining and its application to microarray data analysis
Intelligent Data Analysis
Frequent itemsets for genomic profiling
CompLife'05 Proceedings of the First international conference on Computational Life Sciences
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We report in this paper about our practice of frequent pattern discovery algorithms in the context of mining biological data related to genomic alterations in cancer. A number of frequent item set methods have already been successfully applied to various biological data obtained from large scale analyses (see for instance [4] for SAGE data, [20,22,26] for gene expression data), and all of these have to face the peculiarity of such data wrt standard basket analysis data, namely that the number of observations is low wrt the number of attributes.