搜索资源列表
EM
- EM 算法MATLAB代码，用于数据聚类。-em algorithm which is used in data clustering.
kmeans
- 基本的数据聚类算法，可以进行快速有效的数据聚类，可以有效地进行数据挖掘-Basic data clustering algorithm, can be fast and effective data clustering, data mining can be effectively
KNN
- 数据挖掘导论中的K近邻聚类算法，用C++编写而成。-Introduction to Data Mining of the K neighbors clustering algorithm, using C++ has been prepared by.
Wallpapers-based-on-mean-shift
- 本程序首先把图像由RGB空间转到HSI空间，然后利用彩色图像分割策略以及meanshift算法对图像进行分割最后加入边界合成。其中‘keyprogram.m’文件为主程序，‘meanshift.m’文件为调用函数，实现数据的聚类分割。-The program first the image from the RGB space to HSI space and then using color image segmentation strategy and meanshift image seg
fuzzyPR
- 模糊聚类的经典算法，可以使用UCI数据集进行聚类，该程序附有详细的说明-Classical fuzzy clustering algorithm, you can use the UCI data sets clustering, the program is accompanied by a detailed descr iption of
text-data-mining
- 此程序实现了如何在TXT或WORD文档中进行数据挖掘,在文本中提取有用信息-The realization of this procedure how to TXT or WORD document to carry out data mining, in the text to extract useful information
NcutClustering_7
- Data Clustering with Normalized Cuts
moshishibie
- 先用C-均值聚类算法程序，并用下列数据进行聚类分析。在确认编程正确后，采用蔡云龙书的附录B中表1的Iris数据进行聚类。然后使用近邻法的快速算法找出待分样本X（设X样本的4个分量x1=x2=x3=x4=6；子集数l=3）的最近邻节点和3-近邻节点及X与它们之间的距离。-First C-means clustering algorithm procedures and with the following data for cluster analysis. After confirming t
Cluster
- 使用分解聚类算法在IRIS数据上进行聚类分析，IRIS数据是由鸢尾属植物的三种单独的花的测量结果所组成，模式类别数为3，特征维数是4，每类各有50个模式样本，总共有150个样本。-The use of decomposition in the IRIS data clustering algorithm on the cluster analysis, IRIS data are from the iris flower three separate components of the meas
CLICKS
- clique code with sample data set. clique is a data clustering algorithm which follows hierarchical clustering method.
clustering
- To identify distinguishable clusters of data in an n-dimensional pixel value image. Given: Samples of multi-spectral satellite images -To identify distinguishable clusters of data in an n-dimensional pixel
hddc_toolbox_1.0
- The High Dimensional Data Clustering (HDDC) toolbox contains an efficient unsupervised classifiers for high-dimensional data. This classifier is based on Gaussian models adapted for high-dimensional data. Reference: C. Bouveyron, S. Girard and
High
- This paper presents a clustering approach which estimates the specific subspace and the intrinsic dimension of each class. Our approach adapts the Gaussian mixture model framework to high-dimensional data and estimates the parameters which best
clustering
- Fuzzy Clustering. Problem: To extract rules from data Method: Fuzzy c-means Results: e.g., finding cancer cells
p103-zhang
- Research about Birch data clustering algorithm
Clustering.Algorithms.Research
- 软件学报 2008年论文《聚类算法研究》，作者孙吉贵, 刘杰, 赵连宇。pdf格式，14页。对近年来聚类算法的研究现状与新进展进行归纳总结.一方面对近年来提出的较有代表性的聚类算法,从算法思想、关键技术和优缺点等方面进行分析概括 另一方面选择一些典型的聚类算法和一些知名的数据集,主要从正确率和运行效率两个方面进行模拟实验,并分别就同一种聚类算法、不同的数据集以及同一个数据集、不同的聚类算法的聚类情况进行对比分析.最后通过综合上述两方面信息给出聚类分析的研究热点、难点、不足和有待解决的一些问题.上
Clustering
- Clustering algorithm - data mining
Data-clustering-particle-swarm-optimization
- Data clustering using particle swarm optimization Data clustering using particle swarm optimization
Data-Clustering-with-Normalized-Cuts
- Data clustering using graph cut method
Data-Clustering-with-Normalized-Cuts
- 用ncut算法做的数据聚类，经过测试可以正常使用-data clustering with NCUT