搜索资源列表
LDA
- 线性判别分析（LDA）用于特征选择，可以对数据集或者图像提取有用特征，用于分类或者聚类等机器学习应用中-Linear Discriminant Analysis (LDA) for feature selection, application in dataset or image feature extraction, for classification or clustering applications in machine learning
NJW
- NJW算法，普聚类算法，利用3种拉普拉斯矩阵，作用在自制的数据集上-failed to translate
art
- 用VC++编写的自适应谐振网络（ART）模型的源代码，用于将二值数据集进行无师聚类。-This program contains code implementing the adaptive resonance theory(ART) network. Source code may be found in ART.CPP.
FCMClust
- 采用模糊C均值对数据集data聚为cluster_n类-Using Fuzzy C-Means dataset data gathered for cluster_n classes
Fuzzy-c-means-clustering-algorithm-
- k均值算法是模式识别的聚分类问题，采用模糊C均值对数据集data聚为cluster_n类 -k-means algorithm is a pattern recognition poly classification,by using Fuzzy C-Means data sets, data gathered cluster_n class
PAM
- 利用欧式度量聚类真实数据集和实际数据集用于对各类数据分类 -European metric clustering real data sets and real data sets for various types of data classification
Desktop
- 欧式距离PAM聚类算法，利用欧式度量聚类真实数据集和实际数据集用于对各类数据分类-Euclidean distance the PAM clustering algorithms using the European metric clustering real data sets and real data sets for the various types of data classification
Fuzzyjulei
- 聚类分析，模糊集，适用于多维数据聚类。在研究生期间所做的成功，成功将三位数据实现聚类，并把它运用到交通分类当中。-Cluster analysis, fuzzy sets, is applicable to multi-dimensional data clustering.During the graduate student success, success will be three data clustering, and apply it to the classification o
k-means-iris
- 运用k-means算法对IRIS数据集进行聚类分析-K-means algorithm is appliled to do cluster analysis for IRIS dataset.
BOVW_Class_DEMO
- Matlab实现BOVW模型，特征提取采用SIFT算法，字典学习采用k-means聚类学习，数据集采用UCM21类分类信息-Matlab achieve BOVW model, feature extraction algorithm using SIFT, dictionary learning using k-means clustering, data collection using UCM21 class category
Fcm
- 采用FCM(模糊C均值)算法将数据集data聚为cluster_n类(FCM (fuzzy C means) algorithm is used to aggregate the data set data into cluster_n class)
DPC
- DPC算法的经典实现过程，包含元数据集、决策图以及2维条线下密度聚类(The classical implementation process of DPC algorithm includes metadata set, decision diagram and 2 dimensional lower density clustering)