On discovery of maximal confident rules without support pruning in microarray data
Proceedings of the 5th international workshop on Bioinformatics
CCCS: a top-down associative classifier for imbalanced class distribution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
High Confidence Rule Mining for Microarray Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
CSV: visualizing and mining cohesive subgraphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
An association analysis approach to biclustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining spectrum usage data: a large-scale spectrum measurement study
Proceedings of the 15th annual international conference on Mobile computing and networking
Mining High-Correlation Association Rules for Inferring Gene Regulation Networks
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Predicting protein-protein interactions using numerical associational features
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Finding closed frequent item sets by intersecting transactions
Proceedings of the 14th International Conference on Extending Database Technology
TP+close: mining frequent closed patterns in gene expression datasets
VDMB'06 Proceedings of the First international conference on Data Mining and Bioinformatics
Revenue maximization in spectrum auction for dynamic spectrum access
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
DisClose: discovering colossal closed itemsets via a memory efficient compact row-tree
PAKDD'12 Proceedings of the 2012 Pacific-Asia conference on Emerging Trends in Knowledge Discovery and Data Mining
An efficient and scalable algorithm for mining maximal
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Microarray data typically contains a large number of columns and a small number of rows, which poses a great challenge for existing frequent (closed) pattern mining algorithms that discover patterns in item enumeration space. In this paper, we propose two new algorithms that explore the row enumeration space to mine frequent closed patterns. Several experiments on real-life gene expression data show that the new algorithms are faster than existing algorithms, including CLOSET, CHARM, CLOSET+ and CARPENTER.