A Novel Method for Mining Temporally Dependent Association Rules in Three-Dimensional Microarray Datasets
學年 99
學期 1
發表日期 2010-12-16
作品名稱 A Novel Method for Mining Temporally Dependent Association Rules in Three-Dimensional Microarray Datasets
作品名稱（其他語言）
著者 Liu, Yu-cheng; Lee, Chao-hui; Chen, Wei-chung; Shin, J. W.; Hsu, Hui-huang; Tseng, Vincent S.
作品所屬單位 淡江大學資訊工程學系
出版者 Institute of Electrical and Electronics Engineers(IEEE)
會議名稱
會議地點 Tainan, Taiwan
摘要 Microarray data analysis is a very popular topic of current studies in bioinformatics. Most of the existing methods are focused on clustering-related approaches. However, the relations of genes cannot be generated by clustering mining. Some studies explored association rule mining on microarray, but there is no concrete framework proposed on three-dimensional gene-sample-time microarray datasets yet. In this paper, we proposed a temporal dependency association rule mining method named 3D-TDAR-Mine for three-dimensional analyzing microarray datasets. The mined rules can represent the regulated-relations between genes. Through experimental evaluation, our proposed method can discover the meaningful temporal dependent association rules that are really useful for biologists.
關鍵字 Data Mining;Microarray;Gene Expression Analysis;Association Rule Mining
語言 en
收錄於
會議性質 國際
校內研討會地點
研討會時間 20101216~20101218
通訊作者
國別 TWN
公開徵稿 Y
出版型式
出處 Proc. 2010 International Computer Symposium, pp. 759-764
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