Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Mining the most interesting rules
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
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns by pattern-growth: methodology and implications
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
RainForest - A Framework for Fast Decision Tree Construction of Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Carpenter: finding closed patterns in long biological datasets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining top-K covering rule groups for gene expression data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
On discovery of maximal confident rules without support pruning in microarray data
Proceedings of the 5th international workshop on Bioinformatics
Frequent closed itemset based algorithms: a thorough structural and analytical survey
ACM SIGKDD Explorations Newsletter
CCCS: a top-down associative classifier for imbalanced class distribution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
On Mining Instance-Centric Classification Rules
IEEE Transactions on Knowledge and Data Engineering
Describing differences between databases
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Mining association rules in very large clustered domains
Information Systems
Semantic mining and analysis of gene expression data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
High Confidence Rule Mining for Microarray Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Unifying Framework for Rule Semantics: Application to Gene Expression Data
Fundamenta Informaticae - Special issue ISMIS'05
CSV: visualizing and mining cohesive subgraphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Artificial Intelligence in Medicine
Top-down mining of frequent closed patterns from very high dimensional data
Information Sciences: an International Journal
Calibrated lazy associative classification
SBBD '08 Proceedings of the 23rd Brazilian symposium on Databases
Mining Discriminant Sequential Patterns for Aging Brain
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Exploring ant-based algorithms for gene expression data analysis
Artificial Intelligence in Medicine
A new and useful syntactic restriction on rule semantics for tabular datasets
ICFCA'07 Proceedings of the 5th international conference on Formal concept analysis
A clustering rule-based approach to predictive modeling
Proceedings of the 48th Annual Southeast Regional Conference
Discovering novelty in gene data: from sequential patterns to visualization
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Calibrated lazy associative classification
Information Sciences: an International Journal
Mining interesting association rules in medical images
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Towards ad-hoc rule semantics for gene expression data
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Sequential patterns mining and gene sequence visualization to discover novelty from microarray data
Journal of Biomedical Informatics
Unifying Framework for Rule Semantics: Application to Gene Expression Data
Fundamenta Informaticae - Special issue ISMIS'05
X-Class: Associative Classification of XML Documents by Structure
ACM Transactions on Information Systems (TOIS)
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 datasets typically contain large number of columns but small number of rows. Association rules have been proved to be useful in analyzing such datasets. However, most existing association rule mining algorithms are unable to efficiently handle datasets with large number of columns. Moreover, the number of association rules generated from such datasets is enormous due to the large number of possible column combinations.In this paper, we describe a new algorithm called FARMER that is specially designed to discover association rules from microarray datasets. Instead of finding individual association rules, FARMER finds interesting rule groups which are essentially a set of rules that are generated from the same set of rows. Unlike conventional rule mining algorithms, FARMER searches for interesting rules in the row enumeration space and exploits all user-specified constraints including minimum support, confidence and chi-square to support efficient pruning. Several experiments on real bioinformatics datasets show that FARMER is orders of magnitude faster than previous association rule mining algorithms.