Description: DGCL （An Efficient Density and Grid Based Clustering Algorithm for Large Spatial Database）的实现代码，费了很长时间才实现的-DGCL (An Efficient Density and Grid Based C. lustering Algorithm for Large Spatial Databas e) the realization of code, and a very long time to achieve the Platform: |
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Author:adrian |
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Description: 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是全部算法的实现文件，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。-Form1.cs clustering algorithm is applied DBSCAN (Density-Based S patical Clustering of Application with Noise) example, the two parameters can EPS and MinPts regulation clustering. DBSCAN.cs algorithm is the realization of all documents, the clustering algorithm further information please refer to the "data mining" or books. Clustering sample data from sxdb.mdb, an Access database. Platform: |
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Author:yang |
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Description: cluster in quest聚类算法是基于密度和网格的聚类算法。对于大型数据库的高维数据聚类集合。-cluster in quest clustering algorithm is based on the density of the grid and clustering algorithm. For large database of high-dimensional data clustering pool. Platform: |
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Author:陈妍 |
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Description: 用VC++语言实现了基于距离，基于密度和改进的数据聚类算法。-VC language based on the distance, based on the density and improved data clustering algorithm. Platform: |
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Author:lixiaoqing |
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Description: A general technique for the recovery of signi cant
image features is presented. The technique is based on
the mean shift algorithm, a simple nonparametric pro-
cedure for estimating density gradients. Drawbacks of
the current methods (including robust clustering) are
avoided. Feature space of any nature can be processed,
and as an example, color image segmentation is dis-
cussed. The segmentation is completely autonomous,
only its class is chosen by the user. Thus, the same
program can produce a high quality edge image, or pro-
vide, by extracting all the signi cant colors, a prepro-
cessor for content-based query systems. A 512 512
color image is analyzed in less than 10 seconds on a
standard workstation. Gray level images are handled
as color images having only the lightness coordinate-A general technique for the recovery of sig ni cannot image features is presented. The techni que is based on the mean shift algorithm, a simple nonparametric pro-cedure for estimat ing density gradients. Drawbacks of the curren t methods (including robust clustering) are av oided. Feature space of any nature can be proces sed, and as an example, color image segmentation is dis-cussed. The se gmentation is completely autonomous. only its class is chosen by the user. Thus, the same program can produce a high quality edge image, or pro-vide. by extracting all the signi cannot colors, a prepro- cessor for content-based query syste ms. A 512,512 color image is analyzed in less than 10 seconds on a standard workstation. Gray 4ISR l images are handled as color images having only the lightness c Platform: |
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Author:gggg |
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Description: 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-Form1.cs is the application of clustering algorithm DBSCAN (Density-Based Spatical Clustering of Application with Noise) of the sample, two parameters can MinPts regulation EPS and clustering. Is to achieve DBSCAN.cs document clustering algorithm further information please refer to " data mining" or books related to clustering of data from sample sxdb.mdb, an Access database Platform: |
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Author:allcy |
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Description: DBSCAN是一种性能优越的基于密度的空间聚类算法．利用基于密度的聚类概念，用户只需输入一个参数，DBSCAN算法就能够发现任意形状的类，并可以有效地处理噪声．-DBSCAN is a superior performance of space-based density clustering algorithm. The use of the concept of density-based clustering, the user can enter a parameter, DBSCAN algorithm to be able to find any type of shape, and can effectively deal with noise. Platform: |
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Author:sdsd |
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Description: OPTICS ("Ordering Points To Identify the Clustering Structure") is an algorithm for finding density-based clusters in spatial data. It was
presented by Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel and Jö rg Sander[1]. Its basic idea is similar to DBSCAN,[2] but it
addresses one of DBSCAN s major weaknesses: the problem of detecting meaningful clusters in data of varying density. In order to do so, the
points of the database are (linearly) ordered such that points which are spatially closest become neighbors in the ordering. Additionally, a
special distance is stored for each point that represents the density that needs to be accepted for a cluster in order to have both points belong to
the same cluster. This is represented as a dendrogram. Platform: |
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Author:swap |
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Description: 基于密度的数据挖掘算法，可以有效的去处噪声的干扰。-Clustering the data with Density-Based Scan Algorithm with Noise (DBSCAN) Platform: |
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Author:cluster |
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Description: 论文 在使用无线传感器网络进行目标检测时,如何布置尽可能少的传感器节点而同时实现高的正确检测概率和
低的误警率,是关键问题之一。采用数据融合技术,能实现传感器节点之间的协同,从而大幅提高目标检测精度。提
出了用于目标检测的精度模型,分析了数据融合半径与传感器节点密度之间的关系,设计聚类方法将目标点组织成布
置单元,从高密度单元到低密度单元布置传感器节点覆盖目标区域。仿真结果表明,算法在保证检测精度的同时能有
效减少所使用的传感器节点数目。
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Sensor placement is a key issue in target detection,placing smaller sensors to achieve high detection accuracy
which presented as a specific high detection probability and a low false alarm rate is very important.Data fusion based
collaboration between sensors can boost the level of accuracy in general.In this paper,the accuracy model and the rela-
tionship between the fusion radius and density of sensors were analyzed a quality-threshold clustering algorithm was
employed to organize the surveillance locations into placement units,place sensors in them from the intensive to sparse
one.Simulation results show that this placement algorithm can significantly reduce the total number of sensors with
guaranteed accuracy. Platform: |
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Author:子木 |
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Description: 基于密度的聚类算法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-density based clustering is a basic clustering algorithm in big data. Platform: |
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Author:zcy |
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Description: DBSCAN is a data clustering algorithm based on density. Density-based clustering method has been developed based on the concept of density. Platform: |
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Author:rasool |
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Description: optics 算法作为基于密度的聚类算法的一种重要的改进，十分有借鉴的意义。-Called algorithm for clustering algorithm based on density is an important improvement, very have reference significance. Platform: |
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Author:qiong hiong |
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Description: DBSCAN（Density-Based Spatial Clustering of Applications with Noise，具有噪声的基于密度的聚类方法）是一种基于密度的空间聚类算法。该算法将具有足够密度的区域划分为簇，并在具有噪声的空间数据库中发现任意形状的簇，它将簇定义为密度相连的点的最大集合。
该算法利用基于密度的聚类的概念，即要求聚类空间中的一定区域内所包含对象（点或其他空间对象）的数目不小于某一给定阈值。DBSCAN算法的显著优点是聚类速度快且能够有效处理噪声点和发现任意形状的空间聚类。但是由于它直接对整个数据库进行操作且进行聚类时使用了一个全局性的表征密度的参数，因此也具有两个比较明显的弱点：
（1）当数据量增大时，要求较大的内存支持I/O消耗也很大；
（2）当空间聚类的密度不均匀、聚类间距差相差很大时，聚类质量较差。-DBSCAN (Density-Based Spatial Clustering of Applications with Noise, with noise density-based clustering method) is the density based spatial clustering algorithm. The algorithm will have sufficient density region is divided into clusters, and discover clusters of arbitrary shape in spatial s with noise, the maximum density is defined as a collection it clusters connected points. The algorithm uses the concept of density-based clustering, which called for the number of clusters in space within a certain region containing the object (point or other space objects) is not less than a given threshold. DBSCAN significant advantages of clustering algorithm is fast and effective handling noises and found that spatial clustering of arbitrary shape. However, because it operates directly on the entire and clustering when using a global parameter characterization density, it also has two obvious weaknesses: (1) When the amount of data increases, requiring larger memory supports I/O consumption i Platform: |
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Author:闫鑫 |
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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:wsduqci
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Description: Python密度聚类
最近在Science上的一篇基于密度的聚类算法《Clustering by fast search and find of density peaks》引起了大家的关注(在我的博文“论文中的机器学习算法——基于密度峰值的聚类算法”中也进行了中文的描述)。于是我就想了解下基于密度的聚类算法，熟悉下基于密度的聚类算法与基于距离的聚类算法，如K-Means算法之间的区别。
基于密度的聚类算法主要的目标是寻找被低密度区域分离的高密度区域。与基于距离的聚类算法不同的是，基于距离的聚类算法的聚类结果是球状的簇，而基于密度的聚类算法可以发现任意形状的聚类，这对于带有噪音点的数据起着重要的作用。(The main goal of the density based clustering algorithm is to find high density regions separated by low density regions. Different from distance based clustering algorithm, the clustering results based on distance clustering algorithm are spherical clusters, and density based clustering algorithm can detect clustering of arbitrary shapes, which plays an important role in data with noisy points.) Platform: |
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Author:cjh1882 |
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Description: 一个很好的基于密度的聚类算法，可以借鉴借鉴。(A good density-based clustering algorithm can be used for reference.) Platform: |
Size: 1024 |
Author:katrinaxiu |
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Description: 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(Density based clustering algorithm
It defines the cluster as the largest set of density connected points, and can divide the region with enough high density into clusters, and can find clusters of arbitrary shape in the spatial database of noise) Platform: |
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Author:微染 |
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Search - Density Based Clustering Algorithm