高斯混合密度降解模型(GMDD)是一种基于稳健统计理论的层次聚类方法。
Gaussian Mixture Density Modelling and Decomposition (GMDD) is a hierarchical clustering method based on robust statistical theory.
结合文本数据的语义相似度，给出一种基于语义密度文本数据聚类的方法。
Combined with semantic similarity of text data, this paper gives a method of text data clustering based on semantic density.
在分析常用聚类算法的特点和适应性基础上提出一种基于密度与划分方法的聚类算法。
A clustering algorithm based on density and partitioning method is presented according to the analysis of the strengths and weaknesses of traditional clustering algorithms.
实验结果表明，新算法较基于密度的带噪声数据应用的空间聚类方法(DBSCAN)具有更好的聚类性能。
Experimental results show that the new algorithm has better performance than Density Based Spatial Clustering of Applications with Noise (DBSCAN).
在分析了多种颜色聚类方法的基础上，提出了2种无监督的二维色度平面颜色聚类方法：基于密度的三角化方法和遗传算法优化方法，并通过实验对2种方法进行了比对分析。
After analysing manifold color clustering methods, we present two unsupervised methods based on 2-d chroma plane, i. e, the density-based triangulation method and the GA-based optimization method.
在分析了多种颜色聚类方法的基础上，提出了2种无监督的二维色度平面颜色聚类方法：基于密度的三角化方法和遗传算法优化方法，并通过实验对2种方法进行了比对分析。
After analysing manifold color clustering methods, we present two unsupervised methods based on 2-d chroma plane, i. e, the density-based triangulation method and the GA-based optimization method.
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