Applied multivariate statistical analysis
Applied multivariate statistical analysis
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
The Quadtree and Related Hierarchical Data Structures
ACM Computing Surveys (CSUR)
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering
IEEE Transactions on Fuzzy Systems
Mining association rules in very large clustered domains
Information Systems
Mining association rules from imprecise ordinal data
Fuzzy Sets and Systems
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
Expert Systems with Applications: An International Journal
Extraction of fuzzy rules from fuzzy decision trees: An axiomatic fuzzy sets (AFS) approach
Data & Knowledge Engineering
Learning theory analysis for association rules and sequential event prediction
The Journal of Machine Learning Research
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In this paper we propose to use association rules to mine the association relationships among different genes under the same experimental conditions. These kinds of relations may also exist across many different experiments with various experimental conditions. In this paper, a new approach, called FIS-tree mining, is proposed for mining the microarray data. Our approach uses two new data structures, BSC-tree and FIS-tree, and a data partition format for gene expression level data. Based on these two new data structures it is possible to mine the association rules efficiently and quickly from the gene expression database. Our algorithm was tested using the two real-life gene expression databases available at Stanford University and Harvard Medical School and was shown to perform better than the two existing algorithms, Apriori and FP-Growth.