Description: DBSCAN源代码,是一种典型的基于密度的聚类算法-DBSCAN source code, is a typical example of the density-based clustering algorithm Platform: |
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Author:龙卑鄙 |
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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，并可以在带有“噪声”的空间数据库中发现任意形状的聚类。-DBSCAN is a density-based clustering algorithm. Algorithm change will have enough height to the regional cluster. and to be with the "noise" of the spatial database found clusters of arbitrary shape. Platform: |
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Author:郑可可 |
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Description: 聚类研究，实现了基于距离，基于密度和改进算法-clustering, based on the distance to achieve, based on density and improved algorithm Platform: |
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Author:建国 |
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Description: DBSCAN源代码,是一种典型的基于密度的聚类算法-DBSCAN source code, is a typical example of the density-based clustering algorithm Platform: |
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Author:龙卑鄙 |
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Description: 程序说明：
Form1.cs是应用聚类算法DBSCAN (Density-Based Spatical Clustering of Application with Noise)的示例，可以通过两个参数EPS和MinPts调节聚类。
DBSCAN.cs是实现文件，聚类算法的进一步信息请参考“数据挖掘”或者相关书籍
聚类示例数据来自于sxdb.mdb，一个Access数据库。
已知问题及进一步改进建议：
问题：dbscan.cs行64，SortedList不支持重复键，因此若两个数据点距离相同则无法加入集合
解决：采用人为减小一个微小量，使数据点距离不同且不影响聚类结果
上一解决方案的问题：减小double.Epsilon微小量无助于使SortedList认为两点距离以及不同
解决：采用一个指数增长的微小量，连续重试直至SortedList认为距离已经不同
进一步改进建议：可能通过double的强制转型为内存中的byte类型（假设double型转为8个byte）
然后最后一个byte减去0x01可比较漂亮的解决问题，但是……呵呵，C#中我不会这个操作
也可以自己实现一个SortedList，支持重复键，当然，这，好像是微软应该做的工作了 ^_^
Eric Guo
<http://www.cnblogs.com/ericguo/>
-procedures : Form1.cs clustering algorithm is applied DBSCAN (Density-Based Spati cal Clustering of Application with Noise) example, two parameters can EPS and MinPts regulation clustering. DBSCAN.cs is, the clustering algorithm further information please refer to the "data mining" or books related data clustering example from sxdb.m db, an Access database. Known issues and recommendations for further improvement : : 64 dbscan.cs OK, SortedList not support duplicate keys, and therefore if two data points from the same pool can not be solved by adding : By applying an artificially reduce a small amount of data from different points without clustering results on the impact of a solution of the problem : double.Epsilon small decrease in the amount of helplessness to make that 2:00 S Platform: |
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Author:Huang Yi |
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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，并可以在带有“噪声”的空间数据库中发现任意形状的聚类。-DBSCAN is a density-based clustering algorithm. Algorithm change will have enough height to the regional cluster. and to be with the "noise" of the spatial database found clusters of arbitrary shape. Platform: |
Size: 2048 |
Author: |
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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，并可以在带有“噪声”的空间数据库中发现任意形状的聚类。-DBSCAN is a density-based clustering algorithm. Algorithm change will have enough height to the regional cluster. and to be with the "noise" of the spatial database found clusters of arbitrary shape.-DBSCAN is a density-based clustering algorithm. Changed algorithm will have a high enough regional divided into clusters, and to be with noise found in the spatial database cluster of arbitrary shape.-DBSCAN is a density-based clustering algorithm. Algorithm change will have enough height to the regional cluster. And to be with the noise of the spatial database found clusters of arbitrary shape. Platform: |
Size: 24576 |
Author:蔡宗欣 |
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Description: 经典的基于网格和密度的聚类算法。适合处理大规模数据，效果很好-The classic grid-based clustering algorithm and density. Suited to deal with large-scale data, the effect of good Platform: |
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Author:肖宪 |
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Description: 经典的基于密度的聚类算法，DBSCAN。适合处理球状数据，对大规模数据支持不好-Classical density-based clustering algorithm, DBSCAN. Suited to deal with spherical data, large-scale data to support the poor Platform: |
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Author:肖宪 |
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Description: 用c++实现的CURE聚类算法 这是即DBSCAN算法后出现的基于密度的聚类算法-With c++ Realized CURE clustering algorithm DBSCAN algorithm that is, this is occurring after the density-based clustering algorithm Platform: |
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Author:刘年 |
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Description: 这是一种基于密度的聚类分析算法，可以发现任意形状的簇，可以发现噪声点。-This is a density-based clustering analysis algorithm can find clusters of arbitrary shape can be found noise points. Platform: |
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Author:dys |
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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: Density-Based Spatial Clustering of Applications with Noise (or DBSCAN) is an algorithm used in cluster analysis which is described in this Wikipedia article (http://en.wikipedia.org/wiki/DBSCAN).
The basic idea of cluster analysis is to partition a set of points into clusters which have some relationship to each other. In the case of DBSCAN the user chooses the minimum number of points required to form a cluster and the maximum distance between points in each cluster. Each point is then considered in turn, along with its neighbours, and allocated to a cluster. Platform: |
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Author:Evan |
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Description: clustring(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. Platform: |
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Author:pepe |
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Description: 社会网络分析中的密度估计方法，发表在sigmod2014上的深度文章，有具体算法和实验评价-Social network analysis density estimation method was published in sigmod2014 good text, be able to estimate the social networks of small groups and core. Platform: |
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Author:michael |
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Description: 相比其他的聚类方法，基于密度的聚类方法可以在有噪音的数据中发现各种形状和各种大小的簇。DBSCAN（Ester, 1996）是该类方法中最典型的代表算法之一（DBSCAN获得2014 SIGKDD Test of Time Award）。其核心思想就是先发现密度较高的点，然后把相近的高密度点逐步都连成一片，进而生成各种簇(Compared with other clustering methods, the density based clustering method can find various shapes and sizes of clusters in noisy data. DBSCAN (Ester, 1996) is one of the most typical representation algorithms in this kind of method (DBSCAN obtains 2014 SIGKDD Test of Time Award). The core idea is to find a point with higher density, and then gradually connect the similar high density points to a variety of clusters.) Platform: |
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Author:zkzfengyi |
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Search - density based clustering