Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Fast discovery of association rules
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Context-specific Bayesian clustering for gene expression data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
An Extension to SQL for Mining Association Rules
Data Mining and Knowledge Discovery
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
Expert-Driven Validation of Rule-Based User Models in Personalization Applications
Data Mining and Knowledge Discovery
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Querying multiple sets of discovered rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Post-analysis of learned rules
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Optimizing subset queries: a step towards SQL-based inductive databases for itemsets
Proceedings of the 2004 ACM symposium on Applied computing
Experiences in building a tool for navigating association rule result sets
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
Fully automatic cross-associations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Pruning and Visualizing Generalized Association Rules in Parallel Coordinates
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 2005 ACM symposium on Applied computing
Opportunity map: a visualization framework for fast identification of actionable knowledge
Proceedings of the 14th ACM international conference on Information and knowledge management
Rule interestingness analysis using OLAP operations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Opportunity map: identifying causes of failure - a deployed data mining system
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Visual Analytics: A 2D-3D visualization support for human-centered rule mining
Computers and Graphics
CARIBIAM: Constrained Association Rules using Interactive Biological IncrementAl Mining
International Journal of Bioinformatics Research and Applications
ACM SIGKDD Explorations Newsletter
Mining Association Rule Bases from Integrated Genomic Data and Annotations
Computational Intelligence Methods for Bioinformatics and Biostatistics
Efficient mining of multilevel gene association rules from microarray and gene ontology
Information Systems Frontiers
Exploring ant-based algorithms for gene expression data analysis
Artificial Intelligence in Medicine
Mining High-Correlation Association Rules for Inferring Gene Regulation Networks
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
GUEST EDITORIAL: Computational intelligence in solving bioinformatics problems
Artificial Intelligence in Medicine
Bi-k-bi clustering: mining large scale gene expression data using two-level biclustering
International Journal of Data Mining and Bioinformatics
Finding trees from unordered 0–1 data
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Frequent itemsets for genomic profiling
CompLife'05 Proceedings of the First international conference on Computational Life Sciences
A GO-Based method for assessing the biological plausibility of regulatory hypotheses
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
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The problem of analyzing microarray data became one of important topics in bioinformatics over the past several years, and different data mining techniques have been proposed for the analysis of such data. In this paper, we propose to use association rule discovery methods for determining associations among expression levels of different genes. One of the main problems related to the discovery of these associations is the scalability issue. Microarrays usually contain very large numbers of genes that are sometimes measured in 10,000s. Therefore, analysis of such data can generate a very large number of associations that can often be measured in millions. The paper addresses this problem by presenting a method that enables biologists to evaluate these very large numbers of discovered association rules during the post-analysis stage of the data mining process. This is achieved by providing several rule evaluation operators, including rule grouping, filtering, browsing, and data inspection operators, that allow biologists to validate multiple individual gane regulation patterns at a time. By iteratively applying these operators, biologists can explore a significant part of all the initially generated rules in an acceptable period of time and thus answer biological questions that are of a particular interest to him or her. To validate our method, we tested our system on the microarray data pertaining to the studies of environmental hazards and their influence of gane expression processes. As a result, we managed to answer several questions that were of interest to the biologists that had collected this data.