搜索资源列表
RFCMData1
- rough fuzzy c means algorithm for clustering of data set
KP
- 引入能够处理混合型数据的K-prototypes聚类算法，在此基础上构造了一种基于粒子群优化算法和K-prototypes方法的混合聚类算法-this paper employs the K-prototypes clustering algorithm to deal with mixed valued data, and designs a hybrid clustering algorithm based on particle swarm optimization algorithm a
birch
- BIRCH(Balanced Iterative Reducing and Clustering using Hierarchies)是一个综合的层次聚类算法。它用到了聚类特征(Clustering Feature, CF)和聚类特征树(CF Tree)两个概念，用于概括聚类描述。聚类特征树概括了聚类的有用信息，并且占用空间较元数据集合小得多，可以存放在内存中，从而可以提高算法在大型数据集合上的聚类速度及可伸缩性。 -BIRCH (Balanced Iterative Reducing and
PAM
- PAM（Partitioning Around Medoid,围绕中心点的划分）算法是是划分算法中一种很重要的算法，有时也称为k-中心点算法，是指用中心点来代表一个簇。PAM算法最早由Kaufman和Rousseevw提出，Medoid的意思就是位于中心位置的对象。PAM算法的目的是对n个数据对象给出k个划分。PAM算法的基本思想：PAM算法的目的是对成员集合D中的N个数据对象给出k个划分，形成k个簇，在每个簇中随机选取1个成员设置为中心点，然后在每一步中，对输入数据集中目前还不是中心点的成员根
show_clusters
- show cluster algorithem read data set from file and show graphical clustering
k_algorithm
- k-均值算法（动态聚类法），在数据图像及模式识别，经济，社会学得到广泛应用的算法-k-means algorithm (dynamic clustering method), a widely used algorithm in the data image and pattern recognition, economic, sociological
fcm
- 模糊C均值聚类算法实现数据挖掘的matlab源代码-Fuzzy C-Means clustering algorithm for data mining matlab source code
Kmeans
- Kmeans 算法是聚类分析中使用最为广泛的算法之一，其每个类别均用该类中所有数据的平均值(或加权平均)来表示，这个平均值即被称作聚类中心。该方法虽然不能用于 类别属性的数据，但对于数值属性的数据，它能很好地体现聚类在几何和统计学上的意义。-Kmeans algorithm is the most widely used cluster analysis algorithm, each category with the average of all data in the class (
psocluster
- 改进的微粒群算法来聚类高维数据，重点解决了变量加权问题，聚类质量较高。-Improved particle swarm algorithm to cluster high dimensional data, focused on solving the problem of variable weighting and clustering of high quality
c
- 用C-均值聚类的方法对Iris数据进行聚类分析-Cluster analysis using C-means clustering method on the Iris data
c-junhua
- 用FAMALE.TXT、MALE.TXT和/或test2.txt的数据作为本次实验使用的样本集，利用C均值和分级聚类方法对样本集进行聚类分析，对结果进行分析，从而加深对所学内容的理解和感性认识。-Use FAMALE. TXT, MALE. TXT and/or test2. TXT data as the use of the samples, using C mean and hierarchical clustering method of samples of clustering an
cluster-analysis
- 对“data4.m”数据，采用两种聚类算法聚类，并对结果进行分析。-Uses two types of clustering algorithm clustering "data4.m" data, the results were analyzed.
kclustering
- 使用k均值法对一组数据进行聚类，画出结果图像-Use the k-means clustering, a set of data to draw the resulting image
2D-LDA
- LDA是一种线性降维方法，对原有的高维人脸数据集降维，然后识别，具有很好的聚类和识别效果。有详细的说明-LDA is a linear dimensionality reduction method, the original high-dimensional face data set dimensionality reduction, and then identify clustering and identification. Described in detail
kmeansf
- kmean clustering algorithm. this code will take the training data set (with out class lebel) as input and return the no of optimum centre used in rbfn by using eucleadian distance.
cluster
- 聚类算法，对数据集进行聚类，得出训练后的胜出权值和所有权值-Clustering algorithm to cluster data sets, to win the right to come to training value and ownership of values
julei
- 对“data3.m”数据，采用K-means聚类算法进行聚类。-The " data3.m" data, using K-means clustering algorithm for clustering.
BFO-cluster
- 基本的细菌觅食聚类程序，测试数据集为IRIS数据集。-The basic bacterial foraging clustering procedure, the test data set for the IRIS data set.
meanMina
- MeanShift clustering Code with sample data and plot results
exp7
- LBG分类算法 用初始室心随机法和扰动因子分裂法两种方法，比较不同方法不同参数设置时的分类性能。 -LBG classification algorithm vector quantization: vector normalization within a certain range for a particular type, consists of two steps: first generate a codebook, which is the speech feature v