This paper presents an approach using rough set and Boolean reasoning discretization-reduction to improve the rough self-organizing maps,and using it to analyzz gene expression data.
本文利用粗糙集与布尔逻辑离散约简算法改进了粗糙自组织映射算法,并应用于基因表达数据的分析中。
参考来源 - 粗糙自组织映射在基因表达数据分析中的应用 in C·2,447,543篇论文数据，部分数据来源于NoteExpress
Common approaches to unsupervised learning include k-Means, hierarchical clustering, and self-organizing maps.
无监管学习的常见方法包括k - Means、分层集群和自组织地图。
Introducing diffusing and growing self-organizing maps (DGSOM), we propose a new algorithm called self-organized LLE and give some theoretical analysis.
引入扩散生长型自组织神经网络模型（DGSOM）算法，在深入研究LLE的基础上提出了新的自组织LLE算法并给出理论分析。
It implements two original algorithms specifically designed for clustering short time series together with hierarchical clustering and self-organizing maps.
它实现了两个专为短的时间序列聚类与聚类和自组织映射的原始算法。
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