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: 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/>
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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 (Density-Based Spatial Clustering of Applications with Noise) is a more representative clustering algorithm based on density. And division and hierarchical clustering methods, the maximum density is defined as a collection it is connected to a cluster point, be able to have a sufficiently high density area is divided into clusters, and clusters of arbitrary shape found in noise spatial database. Platform: |
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Description: DBSCAN（Density-Based Spatial Clustering of Applications with Noise）聚类算法，它是一种基于高密度连通区域的、基于密度的聚类算法，能够将具有足够高密度的区域划分为簇，并在具有噪声的数据中发现任意形状的簇。-DBSCAN (Density based Spatial Clustering of Applications with Noise) Clustering algorithm, it is a kind of Based on connected component of high Density, the Clustering algorithm Based on Density, with enough high Density area can be divided into clusters, and has the Noise in the data found in clusters of arbitrary shape. Platform: |
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Search - Density Based Clustering Algorithm