Description: DBSCAN(Density-Based Spatial Clustering of Applacations with Noise)是一个比较有代表性的基于密度的聚类算法。程序用本人独立设计的，保留创意，拷贝不究！-DBSCAN (Density-Based Spatial Clustering of Applacations with Noise) is a more representative density-based clustering algorithm. Procedures for using his own independent design, and to retain creative, copy did not study! Platform: |
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Author:王丽娅 |
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Description: 基于密度聚类算法的实现，用c#语言实现功能比较全面。-Density-based clustering algorithm, with c# language features more comprehensive. Platform: |
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Author:唐智英 |
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Description: 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，本文提出
的基于相对密度的聚类算法，在继承上述优点的基础上。有效地解决了基于密度的聚类结果对参数值过于敏感、参数
值难以设置以厦高密度簇完全被相连的低密度簇所包含等问题。-Density-based clustering algorithm because of its strong resistance to noise and can find clusters of arbitrary shape, etc., in the cluster analysis is widely used, the proposed clustering algorithm based on the relative density, in succession on the basis of the above-mentioned advantages. Effectively solve the density-based clustering results on the parameter value is too sensitive, difficult to set the parameter value to high-density clusters of buildings connected to the low-density clusters are completely contained and other issues. Platform: |
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Author:sdc |
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Description: ：DBSCAN是一个基于密度的聚类算法。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。但DtLqCAN算法没有考虑非空间属性，且DBSCAN算法需扫描空间数据库中每个点的e一
邻域来寻找聚类，这使得DBSCAN算法的应用受到了一定的局限。文中提出了一种基于DBSCAN的算法，可以处理非空
间属性，同时又可以加快聚类的速度。-: DBSCAN is a density-based clustering algorithm. The algorithm has a sufficiently high density area is divided into clusters, and to be with the " noise" found in the spatial database clusters of arbitrary shape. But DtLqCAN algorithm did not consider non-spatial attributes, and spatial database DBSCAN algorithm to be scanned for each point e in the neighborhood to find a cluster, DBSCAN algorithm which makes the application subject to certain limitations. In this paper, an algorithm based on DBSCAN can handle the non-spatial attributes, can also speed up the clustering speed. Platform: |
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Author:sdc |
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Description: Optics聚类算法 OPTICS没有显示地产生一个数据集合簇，它为自动和交互地聚类分析计算一个簇次序。这个次序代表了数据基于密度地聚类结构。它包含地信息，等同于从一个宽广地参数设置范围所获得的基于密度的聚类-Optics do not show clustering algorithm OPTICS to produce a collection of data clusters, it is automatically and interactively computing cluster analysis a cluster order. This order represents the data to cluster based on the density structure. It contains in information from a broadly equivalent range of parameters obtained by density-based clustering Platform: |
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Author:winfrey |
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Description: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a data clustering algorithm
proposed by Martin Ester, Hans-Peter Kriegel, Jö rg Sander and Xiaowei Xu in 1996.[1] It is a density-based
clustering algorithm because it finds a number of clusters starting from the estimated density distribution of
corresponding nodes. DBSCAN is one of the most common clustering algorithms and also most cited in
scientific literature.[2] OPTICS can be seen as a generalization of DBSCAN to multiple ranges, effectively
replacing the parameter with a maximum search radius. Platform: |
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Author:swap |
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Description: 聚类算法实现，基于密度的聚类算法，该算法能够用于对数据进行基于密度的分类-Clustering algorithm, density-based clustering algorithm, which can be used for data classification based on density
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Author:liuchao |
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Description: 基于相对密度的聚类算法（DBSCAN算法），用于处理高密度簇完全被相连的低密度簇所包含的问题-Clustering algorithm based on relative density (DBSCAN algorithm), to handle high-density clusters are completely connected to the problem of low-density cluster contains Platform: |
Size: 3072 |
Author:MAHY |
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Description: java语言编写的 DBSCAN基于密度的聚类算法，可实现对数据点基于密度的聚类-java language DBSCAN density-based clustering algorithm can be realized on density-based clustering of data points Platform: |
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Author:崔序 |
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Description: 数据挖掘算法 dbscan 基于密度的聚类算法 它将簇定义为密度相连的点的最大集合，能够把具有足够高密度的区域划分为簇，并可在噪声的空间数据库中发现任意形状的聚类-Data mining algorithms dbscan density-based clustering algorithm will cluster is defined as the density of points connected to the largest collection of regional division able to have a high enough density of clusters, clusters of arbitrary shape can be found in the noise of the spatial database Platform: |
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Author:孙伟 |
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Description: 利用经典的基于密度的聚类算法DBSCAN实现对三类高斯数据实现分类-Classical density-based clustering algorithm to achieve the three Gaussian DBSCAN data to classify Platform: |
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Author:任璐 |
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Description: 基于密度的聚类算法，对于非球型簇非常有效，可以得到各种类别-Density-based clustering algorithm for non-spherical cluster is very effective, you can get a variety of categories Platform: |
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Author:大沙 |
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Description: 本论文在对各种算法深入分析的基础上，尤其在对基于密度的聚类算法、基于层次的聚类算法和基于划分的聚类算法的深入研究的基础上，提出了一种新的基于密度和层次的快速聚类算法。该算法保持了基于密度聚类算法发现任意形状簇的优点，而且具有近似线性的时间复杂性，因此该算法适合对大规模数据的挖掘。理论分析和实验结果也证明了基于密度和层次的聚类算法具有处理任意形状簇的聚类、对噪音数据不敏感的特点，并且其执行效率明显高于传统的DBSCAN算法。-Based on the analysis on clustering algorithms especially on Density-Based clustering algorithm、Hierarchical-Based clustering algorithm and Partition-Based clustering algorithm, in this paper, a new kind of clustering algorithm that is clustering based on density and hierarchy is presented. This algorithm keeps the ability of density based clustering method’s good features, and it can reach high efficiency because of its linear time complexity, so it can be used in mining very large s. Both theory analysis and experimental results confirm that this algorithm can discover clusters with arbitrary shape and is insensitive to noise data. In the meanwhile, its executing efficiency is much higher than traditional DBSCAN algorithm. Platform: |
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Author:wfyan |
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Description: DBSCAN是一个基于密度的聚类算法,改算法将具有足够高度的区域划分为簇-DBSCAN is a density based clustering algorithm, the algorithm will have enough height area is divided into clusters Platform: |
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Author:delagation |
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Description: DBSCAN is a density based clustering algorithm, the algorithm will have enough height area is divided into clusters Platform: |
Size: 2048 |
Author:aop@880 |
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Description: DBSCAN是一个基于密度的聚类算法,改算法将具有足够高度的区域划分为簇-DBSCAN is a density based clustering algorithm, the algorithm will have enough height area is divided into clusters Platform: |
Size: 2048 |
Author:Salome |
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Description: DBSCAN是一个基于密度的聚类算法,改算法将具有足够高度的区域划分为簇-DBSCAN is a density based clustering algorithm, the algorithm will have enough height area is divided into clusters Platform: |
Size: 2048 |
Author:Chrisyian |
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Description: DBSCAN(Density-Based Spatial Clustering of Applications with Noise)是一个比较有代表性的基于密度的聚类算法。与划分和层次聚类方法不同，它将簇定义为密度相连的点的最大集合，能够把具有足够高密度的区域划分为簇，并可在噪声的空间数据库中发现任意形状的聚类。(DBSCAN is a representative density based clustering algorithm. Unlike the partition and hierarchical clustering method, it will be the largest collection of clusters is defined as the density connected points, to have sufficient division of high density clusters, clustering and discovery of arbitrary shape in spatial database in noise.) Platform: |
Size: 1024 |
Author:扑棱扑棱
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Search - Density Based Clustering Algorithm