Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Handling very large numbers of association rules in the analysis of microarray data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficiently Mining Gene Expression Data via a Novel Parameterless Clustering Method
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Interactive Gene Clustering--A Case Study of Breast Cancer Microarray Data
Information Systems Frontiers
Predicting Protein-Protein Interactions by Association Mining
Information Systems Frontiers
A data mining approach to database compression
Information Systems Frontiers
A Novel Similarity-Based Fuzzy Clustering Algorithm by Integrating PCM and Mountain Method
IEEE Transactions on Fuzzy Systems
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Some recent studies have shown that association rules can reveal the interactions between genes that might not have been revealed using traditional analysis methods like clustering. However, the existing studies consider only the association rules among individual genes. In this paper, we propose a new data mining method named MAGO for discovering the multilevel gene association rules from the gene microarray data and the concept hierarchy of Gene Ontology (GO). The proposed method can efficiently find out the relations between GO terms by analyzing the gene expressions with the hierarchy of GO. For example, with the biological process in GO, some rules like Process A (up) 驴 Process B (up) cab be discovered, which indicates that the genes involved in Process B of GO are likely to be up-regulated whenever those involved in Process A are up-regulated. Moreover, we also propose a constrained mining method named CMAGO for discovering the multilevel gene expression rules with user-specified constraints. Through empirical evaluation, the proposed methods are shown to have excellent performance in discovering the hidden multilevel gene association rules.