Clustering Algorithms for Time Series Gene Expression in Microarray Data
نویسندگان
چکیده
illustrations, 75 numbered references. Clustering techniques are important for gene expression data analysis. However, efficient computational algorithms for clustering time-series data are still lacking. This work documents two improvements on an existing profile-based greedy algorithm for short time-series data; the first one is implementation of a scaling method on the pre-processing of the raw data to handle some extreme cases; the second improvement is modifying the strategy to generate better clusters. Simulation data and real microarray data were used to evaluate these improvements; this approach could efficiently generate more accurate clusters. A new feature-based algorithm was also developed in which steady state value; overshoot, rise time, settling time and peak time are generated by the 2 nd order control system for the clustering purpose. This feature-based approach is much faster and more accurate than the existing profile-based algorithm for long time-series data. also thank Dr.Xiang Gao for her support as committee member of my thesis. I am so grateful for the support of my families, my fiancee Yixuan Liu, my friends and colleagues Guangchunciate all the people I know for helping me finish the thesis.
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