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.
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。
Meanwhile classic algorithms take only distance or density as the norm applied in clustering, so it is unreasonable and undesirable.
该算法将具有足够高密度的区域划分为簇，并可以在带有“噪声”的空间数据库中发现任意形状的聚类。
It can handle spatial data and spot any-shape clusters in a noised spatial database by dividing them into clusters with high enough density.
提出了一种多密度网格聚类算法gdd。
This paper presents a grid - based clustering algorithm for multi - density (GDD).
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.
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.
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.
DBSCAN是一个基于密度的聚类算法。
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.
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.
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.
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。
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.
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.
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.
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.
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.
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.
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.
利用聚类算法延长高密度无线传感器网络的生命周期是一个研究热点。
Using clustering algorithm to prolong the highly dense sensor network lifetime is an important issue.
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).
实验结果表明，该算法能快速、有效地识别任意形状、不同大小和密度聚类的边界点。
Experimental results show that the algorithm can identify boundary points in noisy datasets containing clustering of different shapes and sizes effectively and efficiently.
实验结果表明，该算法能快速、有效地识别任意形状、不同大小和密度聚类的边界点。
Experimental results show that the algorithm can identify boundary points in noisy datasets containing clustering of different shapes and sizes effectively and efficiently.
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