Sciweavers

Share
VLDB
2004
ACM

Semantic Mining and Analysis of Gene Expression Data

11 years 7 months ago
Semantic Mining and Analysis of Gene Expression Data
Association rules can reveal biological relevant relationship between genes and environments / categories. However, most existing association rule mining algorithms are rendered impractical on gene expression data, which typically contains thousands or tens of thousands of columns (gene expression levels), but only tens of rows (samples). The main problem is that these algorithms have an exponential dependence on the number of columns. Another shortcoming is evident that too many associations are generated from such kind of data. To this end, we have developed a novel depth-first row-wise algorithm FARMER [2] that is specially designed to efficiently discover and cluster association rules into interesting rule groups (IRGs) that satisfy user-specified minimum support, confidence and chi-square value thresholds on biological datasets as opposed to finding association rules individually. Based on FARMER, we have developed a prototype system that integrates semantic mining and visual...
Xin Xu, Gao Cong, Beng Chin Ooi, Kian-Lee Tan, Ant
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where VLDB
Authors Xin Xu, Gao Cong, Beng Chin Ooi, Kian-Lee Tan, Anthony K. H. Tung
Comments (0)
books