Description: 经典浓度聚类算法OPTICS，matlab实现，简单易懂，可以运行-Classic Concentration clustering algorithm OPTICS, matlab implementation, easy to understand, you can run Platform: |
Size: 1024 |
Author:Liang Ge |
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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: Ordering Points To Identify the Clustering Structure-Abstract:
Cluster analysis is a primary method for database mining. Platform: |
Size: 243712 |
Author:陈瑜 |
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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: |
Size: 173056 |
Author:swap |
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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: |
Size: 139264 |
Author:swap |
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Description: optics算法 [RD,CD,order]=optics(x,k)； Aim: Ordering objects of a data set to obtain the clustering structure
Input: x - data set (m,n) m-objects 对象数, n-variables 变量数
k - number of objects in a neighborhood of the selected object-OPTICS Ordering Points To Identify the Clustering Structure Platform: |
Size: 317440 |
Author:haodiangei |
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Description: 基于余弦聚类的OPTICS聚类算法，能够用于文本聚类-This is the OPTICS clustering algorithm based on cosine distance which can be used in text clustering. Platform: |
Size: 1024 |
Author:liguoyin |
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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: |
Size: 1024 |
Author:qiong hiong |
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Description: optics密度聚类源码，简单容易理解，止血药修改ReadTxt.m中的load的传入，就可以直接运行。-Called density clustering source, simple and easy to understand, hemostatic changes ReadTxt. M of the introduction of the load, can be directly run. Platform: |
Size: 1024 |
Author:赵博 |
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Description: OPTICS聚类算法，分为主程序、副程序和数据(OPTICS clustering algorithm, which is divided into main program, sub-program and data) Platform: |
Size: 2048 |
Author:哈哈啊不 |
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