Two novel interestingness measures for gene association rule mining

Meihua Wang; Shumin Wu; Ruichu Cai

doi:10.1007/s00521-012-1005-3

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Two novel interestingness measures for gene association rule mining

Meihua Wang Shumin Wu Ruichu Cai

Year: 2012 Journal: Neural Computing and Applications Vol: 23 (3-4)Pages: 835-841 Publisher: Springer Science+Business Media

DOI: 10.1007/s00521-012-1005-3

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Keywords:

Interpretability Association rule learning Computer science Data mining Measure (data warehouse) KEGG Computational biology Curse of dimensionality Gene Artificial intelligence Machine learning Biology Gene expression Gene ontology Genetics

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Gene expression and cancer classification

Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology

Data Mining Algorithms and Applications

Physical Sciences → Computer Science → Information Systems

Cancer-related molecular mechanisms research

Life Sciences → Biochemistry, Genetics and Molecular Biology → Cancer Research

Two novel interestingness measures for gene association rule mining

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