Aiming at solving the asymmetry of the distribution of particles, we classified the particles in images by means of density clustering analysis.
并且针对图像中粒子分布的不均匀性，提出了利用密度聚类分析对图像中的粒子密度进行分类。
In order to improve the training efficiency, an advanced Fuzzy Support Vector Machine (FSVM) algorithm based on the density clustering (DBSCAN) is proposed.
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。
To solve the problem that support vector machine(SVM) can only classify the small samples set, a new algorithm which applied SVM to density clustering is proposed.
为了解决支持向量机的分类仅应用于较小样本集的问题，提出了一种密度聚类与支持向量机相结合的分类算法。
Meanwhile classic algorithms take only distance or density as the norm applied in clustering, so it is unreasonable and undesirable.
同时，经典算法都单纯以距离或密度作为划分聚类的标准，因此存在很大的局限性。
A new heuristic density-based ant colony clustering algorithm (HDACC) is presented.
提出一种基于密度的启发性群体智能聚类算法。
And then in MTM system, we get region color features from the proposed color density-based clustering.
然后在MTM系统中，利用颜色密度聚类的方法得到区域颜色的特征。
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.
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。
Gaussian Mixture Density Modelling and Decomposition (GMDD) is a hierarchical clustering method based on robust statistical theory.
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。
Combined with semantic similarity of text data, this paper gives a method of text data clustering based on semantic density.
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。
This paper introduced a density grid-based data stream clustering algorithm.
提出了一种基于密度网格的数据流聚类算法。
With strong ability of discovery of arbitrary shape clusters and handling noise, density based clustering is one of primary methods for data mining.
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。
This paper presents a grid - based clustering algorithm for multi - density (GDD).
提出了一种多密度网格聚类算法gdd。
A clustering algorithm based on density and partitioning method is presented according to the analysis of the strengths and weaknesses of traditional clustering algorithms.
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 fast clustering algorithm with adaptive density based on homogeneous grid is proposed.
提出了一种基于均匀网格的自适应密度快速聚类新算法。
This method USES kernel density estimation model to construct the approximate density function, and takes hill climbing strategy to extract clustering patterns.
该方法采用核密度估计模型来构造近似密度函数，利用爬山策略来提取聚类模式。
DBSCAN algorithm is an outstanding representative of density based on clustering algorithms.
DBSCAN是基于密度的聚类算法的一个典型代表。
DBSCAN is a spatial clustering algorithm based on density.
DBSCAN是一个基于密度的聚类算法。
In this paper, a fast density based clustering algorithm is developed, which considerably speeds up the original DBSCAN algorithm.
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。
According to the characteristics of gene expression data, a high accurate density-based clustering algorithm called DENGENE was proposed.
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。
DBSCAN is a density based clustering algorithm that can efficiently discover clusters of arbitrary shape and can effectively handle noise.
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。
To solve the problem, a fuzzy clustering based on predator prey PSO algorithm is presented, which is using density function to initialize cluster centre.
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。
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。
After discussing the concepts, techniques and algorithms about clustering, a grid and density based cluster algorithm was proposed.
讨论数据挖掘中聚类的相关概念、技术和算法。
The paper mainly discusses a clustering algorithm based on density and grid in data mining, which has high clustering efficiency and low time complexity.
该文主要讨论数据挖掘中一种基于密度和网格的聚类分析算法及其在客户关系管理中的应用。
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。
Experimental results show that the new algorithm has better performance than Density Based Spatial Clustering of Applications with Noise (DBSCAN).
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。
Then, the processes of computing the vector values of POI objects are discussed by the methods of questionnaire survey, multi-density spatial clustering and data normalization respectively.
然后，分别讨论了利用问卷调查、多密度空间聚类和数据规格化的方法计算POI对象的各项显著性指标值的过程；
Then, the processes of computing the vector values of POI objects are discussed by the methods of questionnaire survey, multi-density spatial clustering and data normalization respectively.
然后，分别讨论了利用问卷调查、多密度空间聚类和数据规格化的方法计算POI对象的各项显著性指标值的过程；
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