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: |
Size: 73728 |
Author:建国 |
Hits:
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: |
Size: 26624 |
Author:yang |
Hits:
Description: 二维的DBSCAN聚类算法，输入（x,y）数组，搜索半径Eps，密度搜索参数Minpts。输出: Clusters，每一行代表一个簇，形式为簇的对象对应的原数据集的ID-two-dimensional clustering algorithm, the input (x, y) array, search radius Eps. Minpts density search parameters. Output : Clusters, each firm on behalf of a cluster, in the form of clusters of objects corresponding to the original data set ID Platform: |
Size: 1024 |
Author:胡瑞飞 |
Hits:
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: |
Size: 9216 |
Author:龙卑鄙 |
Hits:
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: |
Size: 26624 |
Author:Huang Yi |
Hits:
Description: Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
一个可以实现多种方法分类的软件，利用各个
对象的属性。决策树，距离、密度等-Weka is a collection of machine learning al gorithms for data mining tasks. The algorithms can either be applied directly to a dataset or ca lled from your own Java code. Weka contains tool 's for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for d eveloping new machine learning schemes. can be a real Categories are various methods of software, using all the attributes of objects. Decision Tree, distance, density, etc. Platform: |
Size: 15446016 |
Author:马何坛 |
Hits:
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: |
Size: 4096 |
Author:陈妍 |
Hits:
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: |
Hits:
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: |
Size: 73728 |
Author:lixiaoqing |
Hits:
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: |
Size: 17408 |
Author:gggg |
Hits:
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: |
Size: 2092032 |
Author:adrian |
Hits:
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 |
Hits:
Description: 现有的几个网络拓扑随机发生器，其实很难生成理想的网络拓扑结构，其主要原因在于很难控制节点的疏密和间距。我们提出来的这个改进算法，在随机抛撒节点的时候使用了K均值聚类，由本算法作为网络拓扑发生器，网络节点分布均匀且疏密得当，边的分布也比较均衡-The few existing random network topology generator, is in fact very difficult to generate the desired network topology, the main reason it is difficult to control the node density and spacing. We put forward the improved algorithm, throw in random nodes when using the K-means clustering, by the algorithm as a network topology generator, network nodes and spacing evenly distributed properly, the edge of a more balanced distribution of Platform: |
Size: 2048 |
Author:ben |
Hits:
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: |
Size: 197632 |
Author:肖宪 |
Hits:
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: |
Size: 6144 |
Author:肖宪 |
Hits:
Description: ClustanGraphics聚类分析工具。提供了11种聚类算法。
Single Linkage (or Minimum Method, Nearest Neighbor)
Complete Linkage (or Maximum Method, Furthest Neighbor)
Average Linkage (UPGMA)
Weighted Average Linkage (WPGMA)
Mean Proximity
Centroid (UPGMC)
Median (WPGMC)
Increase in Sum of Squares (Ward s Method)
Sum of Squares
Flexible (ß space distortion parameter)
Density (or k-linkage, density-seeking mode analysis) -ClustanGraphics clustering analysis tools. Provides 11 kinds of clustering algorithms. Single Linkage (or Minimum Method, Nearest Neighbor) Complete Linkage (or Maximum Method, Furthest Neighbor) Average Linkage (UPGMA) Weighted Average Linkage (WPGMA) Mean ProximityCentroid (UPGMC) Median (WPGMC) Increase in Sum of Squares (Ward s Method) Sum of SquaresFlexible (? space distortion parameter) Density (or k-linkage, density-seeking mode analysis) Platform: |
Size: 56320 |
Author:wangyexin |
Hits:
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: |
Size: 2048 |
Author:dys |
Hits:
Search - clustering density