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Efficiently mining gene expression data via a novel parameterless clustering method

机译:通过新型无参数聚类方法有效地挖掘基因表达数据

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摘要

Clustering analysis has been an important research topic in the machine learning field due to the wide applications. In recent years, it has even become a valuable and useful tool for in-silico analysis of microarray or gene expression data. Although a number of clustering methods have been proposed, they are confronted with difficulties in meeting the requirements of automation, high quality, and high efficiency at the same time. In this paper, we propose a novel, parameterless and efficient clustering algorithm, namely, correlation search technique (CST), which fits for analysis of gene expression data. The unique feature of CST is it incorporates the validation techniques into the clustering process so that high quality clustering results can be produced on the fly. Through experimental evaluation, CST is shown to outperform other clustering methods greatly in terms of clustering quality, efficiency, and automation on both of synthetic and real data sets.
机译:由于广泛的应用,聚类分析已成为机器学习领域的重要研究课题。近年来,它甚至已成为对微阵列或基因表达数据进行计算机分析的有价值和有用的工具。尽管已经提出了许多聚类方法,但是它们在同时满足自动化,高质量和高效率的要求方面面临困难。在本文中,我们提出了一种新颖的,无参数的,有效的聚类算法,即相关搜索技术(CST),它适合于基因表达数据的分析。 CST的独特之处在于它将验证技术整合到了聚类过程中,从而可以即时生成高质量的聚类结果。通过实验评估,CST在合成数据集和真实数据集的聚类质量,效率和自动化方面均表现出明显优于其他聚类方法的效果。

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