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Title: Data Mining Techniques in Gene Expression Data Analysis
Authors: XU XIN
Keywords: rule group, associative classification, non-linear correlation, shifting-and-scaling correlation, gene expression analysis
Issue Date: 27-Sep-2006
Citation: XU XIN (2006-09-27). Data Mining Techniques in Gene Expression Data Analysis. ScholarBank@NUS Repository.
Abstract: In this thesis, we systematically study the existing problems of state-of-the-art data mining algorithms for gene expression data analysis in class association rule mining, associative classification and subspace clustering. Specifically, we propose the concept of top-k covering rule groups (TopKRGs) for each gene expression sample, and design a row-wise mining algorithm to discover TopKRGs efficiently. We further develop a new associative classifier of the top k covering rule groups. To address the nonlinear correlation problem and the shifting-and-scaling correlation problem, we introduce our Curler and RegMiner algorithms respectively to identify the subset of genes which exhibit non-linear or shifting-and-scaling correlation patterns across a subset of conditions. Extensive experimental studies are conducted on synthetic and real-life datasets. The experimental results show the effectiveness and efficiency of our algorithms. While we mainly use gene expression data in our study, our algorithms can also be applied to high-dimensional data in other domains.
Appears in Collections:Ph.D Theses (Open)

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