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Clustering analysis of gene expression data based on semi-supervised Visual Clustering Algorithm

机译:基于半监督视觉聚类算法的基因表达数据聚类分析

摘要

When gene expression datasets contain some labeled data samples, the labeled information should be incorporated into clustering algorithm such that more reasonable clustering results can be achieved. In this paper, a novel semi-supervised clustering algorithm, Semi-supervised Iterative Visual Clustering Algorithm (Semi-IVCA), is presented to tackle with such datasets. The new algorithm first constructs the visual sampling image of the dataset based on visual theorem and obtains its attractors using the gradient learning rules, where each attractor denotes a cluster of the dataset. Then the new algorithm introduces an iterative clustering procedure to realize the semi-supervised learning. The new algorithm is a generalization of the current Visual Clustering Algorithm (VCA) presented by authors. Except for the advantage that Semi-IVCA can effectively utilize the labeled data information in clustering, it is robust and insensitive to initialization, and it has strong parameter learning capability and good interpretation for the clustering results. When the new algorithm Semi-IVCA is applied to the artificial and real gene expression datasets, the experimental results confirm the above advantages of algorithm Semi-IVCA.
机译:当基因表达数据集包含一些标记的数据样本时,应将标记的信息纳入聚类算法,以便获得更合理的聚类结果。在本文中,提出了一种新颖的半监督聚类算法,即半监督迭代视觉聚类算法(Semi-IVCA)来解决此类数据集。新算法首先基于视觉定理构造数据集的视觉采样图像,并使用梯度学习规则获得其吸引子,其中每个吸引子表示数据集的聚类。然后,新算法引入了迭代聚类过程,以实现半监督学习。新算法是作者提出的当前视觉聚类算法(VCA)的概括。除了Semi-IVCA可以在聚类中有效利用标记数据信息的优点之外,它还具有健壮性和对初始化不敏感的特性,并且具有强大的参数学习能力和对聚类结果的良好解释。当将新算法Semi-IVCA应用于人工和真实基因表达数据集时,实验结果证实了Semi-IVCA算法的上述优点。

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