U*C: Self-organized Clustering with Emergent Feature Maps
U*C: Self-organized Clustering with Emergent Feature Maps
复制标题
U*C：具有紧急特征图的自组织聚类
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
A. Ultsch
中科院分区：
文献类型：
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作者：
A. Ultsch
提出了一种新的基于网格投影的聚类算法。这种算法称为U*C，它使用距离信息和密度结构。集群的数量是自动确定的。所发现的聚类的有效性可以通过网格顶部的U* 矩阵可视化来判断。一个U* 矩阵给出了一个高维数据集的距离和密度结构的组合可视化。对于一组关键的聚类问题，它表明，U*C聚类是上级标准的聚类算法，如Kmeans和层次聚类。
A new clustering algorithm based on grid projections is proposed. This algorithm, called U*C, uses distance information together with density structures. The number of clusters is determined automatically. The validity of the clusters found can be judged by the U*-Matrix visualization on top of the grid. A U*-Matrix gives a combined visualization of distance and density structures of a high dimensional data set. For a set of critical clustering problems it is demonstrated that U*C clustering is superior to standard clustering algorithms such as Kmeans and hierarchical clusterings.