DBSCAN是一个基于密度的聚类算法。
DBSCAN是基于密度的聚类算法的一个典型代表。
DBSCAN algorithm is an outstanding representative of density based on clustering algorithms.
根据基因表达数据的特点，提出一种高精度的基于密度的聚类算法DENGENE。
According to the characteristics of gene expression data, a high accurate density-based clustering algorithm called DENGENE was proposed.
基于密度的聚类算法因其抗噪声能力强和能发现任意形状的簇等优点，在聚类分析中被广泛采用。
With strong ability of discovery of arbitrary shape clusters and handling noise, density based clustering is one of primary methods for data mining.
迄今为止人们提出了许多用于大规模数据库的聚类算法。基于密度的聚类算法DBSCAN就是一个典型代表。
In this paper, a fast density based clustering algorithm is developed, which considerably speeds up the original DBSCAN algorithm.
在分析与研究现有聚类算法的基础上，提出了一种基于密度和自适应密度可达的改进算法。
On the basis of analysis and research of traditional clustering algorithms, a clustering algorithm based on density and adaptive density-reachable is presented in this paper.
提出一种基于密度的启发性群体智能聚类算法。
A new heuristic density-based ant colony clustering algorithm (HDACC) is presented.
基于网格的多密度聚类算法不仅能够对数据集进行正确的聚类，同时还能有效的进行孤立点检测，有效的解决了传统多密度聚类算法中不能有效识别孤立点和噪声的缺陷。
GDD algorithm can not only clusters correctly but find outliers in the dataset, and it effectively solves the problem that traditional grid algorithms can cluster only or find outliers only.
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.
提出了一种基于均匀网格的自适应密度快速聚类新算法。
A fast clustering algorithm with adaptive density based on homogeneous grid is proposed.
提出了一种基于密度网格的数据流聚类算法。
This paper introduced a density grid-based data stream clustering algorithm.
为了提高模糊支持向量机在数据集上的训练效率，提出一种改进的基于密度聚类(DBSCAN)的模糊支持向量机算法。
In order to improve the training efficiency, an advanced Fuzzy Support Vector Machine (FSVM) algorithm based on the density clustering (DBSCAN) is proposed.
在分析与研究现有聚类算法的基础上，提出一种基于密度和自适应密度可达的改进算法。
On the basis of the analysis and research of traditional clustering algorithms, a clustering algorithm based on density and adaptive density-reachable is presented.
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).
DBSCAN是一种基于密度的空间聚类算法，在处理空间数据时具有快速、有效处理噪声点和发现任意形状的聚类等优点。
DBSCAN is a density based clustering algorithm that can efficiently discover clusters of arbitrary shape and can effectively handle noise.
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.
为解决此问题，提出一种基于捕食-被捕食的粒子群优化模糊聚类算法且聚类中心采用密度函数初始化。
To solve the problem, a fuzzy clustering based on predator prey PSO algorithm is presented, which is using density function to initialize cluster centre.
针对高维聚类算法——相交网格划分算法GCOD存在的缺陷，提出了基于密度度量的相交网格划分聚类算法IGCOD。
To overcome the shortcomings of the GCOD, a high-dimensional clustering algorithm for data mining, the paper proposes an intersected grid clustering algorithm based on density estimation (IGCOD).
针对高维聚类算法——相交网格划分算法GCOD存在的缺陷，提出了基于密度度量的相交网格划分聚类算法IGCOD。
To overcome the shortcomings of the GCOD, a high-dimensional clustering algorithm for data mining, the paper proposes an intersected grid clustering algorithm based on density estimation (IGCOD).
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