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AdaBoost
- Adaboost是一种迭代算法，其核心思想是针对同一个训练集训练不同的分类器(弱分类器)，然后把这些弱分类器集合起来，构成一个更强的最终分类器（强分类器）。-Adaboost is an iterative algorithm, the core idea is the same training set for training different classifiers (weak classifiers), then these weak classifiers together to f
K_CenterPoint_PAM
- k中心点算法，也就是PAM算法。是数据挖掘中聚类分析的一种手段，用途广泛。-k center algorithm, i.e. PAM algorithm. Data mining is a means of cluster analysis, and versatile.
K-Means
- K-MENAS算法是最简单的聚类算法，适合对于初学者的学习和改进使用-K-MENAS algorithm is a clustering algorithm is the most simple, suitable for beginners to learn and use improved
FCM
- FCM算法是模糊聚类算法，是在对K-MEANS算法的基础上改进的聚类算法，比L-MEANS算法有更好的效果-FCM algorithm is a fuzzy clustering algorithm, clustering algorithm is improved based on the K-MEANS algorithm, the L-MEANS algorithm is better than the effect of it
k-means-original
- k-menas算法是最简单的聚类算法，算法内详细介绍了各个函数和功能，对初学者很有借鉴意义-K-MEANS algorithm is one of the most simple clustering algorithm, there are different form, this is one of them, a reference for beginners
gas-meter-reading
- 无线通信技术已经运用在我们生活的每个角落，然而由于现实传输的复杂性和不确定性，同时无线信道存在着慢衰落、快衰落效应，会一定程度上造成资源的浪费和通信质量的下降。协同通信是近些年来无线通信研究的热点，它相对于传统无线通信技术在降低成本、节约资源等方面具有明显的优势。协同通信中，中继选择算法根据遵循的标准不同有着不同的算法-Wireless communication technology has been applied in every corner of our life. However,
kmeans
- k-means算法是文本聚类经典算法，也是数据挖掘十大经典算法之一。k-means算法Java实现。-k-means algorithm is a classical algorithm text clustering, data mining is one of the ten classic algorithms. k-means algorithm is implemented in Java.
FCM
- 核聚类算法：聚类是将一组给定的未知类标号的样本分成内在的多个类别，使得同一类中 的样本具有较高的相似度，而不同类中的样本差别大。侧重于软聚类（模糊C-均值——FCM），但其描述手段同样适合于硬聚 类（HCM）等同类问题。-Clustering algorithm: cluster is a group of unknown samples given class label into internal multiple categories, so that the same class
Apriori
- Apriori关联规则算法 运行环境: JDK1.7.0_25 数据集格式： 一条记录一行，各项以 , 分隔-Apriori association rules algorithm Operating environment: JDK1.7.0_25 Dataset format: A record of his party, the order , to separate
SVDFeature
- 自己开发的一个机器学习算法，整合了监督学习，非监督学习-A machine learning algorithm own development, integration of supervised learning, unsupervised learning
k-means-cluster
- 分类算法中k均值分类算法，可以用于简单的模式识别中去。-Classification algorithm of k-means classification algorithm, which can be used in a simple pattern recognition.
pca
- pca算法原理介绍和仿真代码，主要用于数据的聚类，代码时用于图像上的聚类过程，聚类效果很好，就是有点慢-pca algorithm introduces the principle and simulation code, mainly for clustering data, a clustering process images on-time code, clustering works well, is a bit slow
CART
- CART是决策树算法的一种，是数据挖掘的重要组成部分。-CART is a decision tree algorithm, is an important part of data mining.
SVM_liner_kernel
- 这是SVM算法的一个简单应用，用SVM进行线性二分类。此算法可以帮助大家从基础上认识SVM，从而进行编程。-This is a simple application of SVM algorithm, using a linear SVM binary classification. This algorithm can help you recognize the basis of SVM, which can be programmed.
kmeansPP
- kmeans 算法的改进算法 名字为kmeans++算法，参考的文献为 kmeans++： an efficient algorithm。。。。算法他人所写，这里上传，以方便同行传阅-this is a algorithm named k_means++. reference the paper : an efficient algorithm ...
algorithm
- 多线性SVM分类器，在实现分类的同时，能很好的聚类-Multi-linear SVM classifier to classify the same time, can be a good clustering
SSC_ADMM_V3.a
- 最新的聚类算法，很好地解决混沌流形问题。-The latest clustering algorithm, a very good solution to the problem of chaotic manifold.
A-new_cluster_algorithm
- 2014年 6 月份，Alex Rodriguez 和 Alessandro Laio 在 Science 上发表了一篇名为《Clustering by fast search and find of density peaks》的文章，为聚类算法的设计提供了一种新的思路。虽然文章出来后遭到了众多读者的质疑，但整体而言，新聚类算法的基本思想很新颖，且简单明快，值得学习。-June 2014, Alex Rodriguez and Alessandro Laio on Science publis
semi-supervised-cluster-algorithm
- 半监督聚类是利用少量的标记数据提高聚类算法的性能，文中综述了半监督聚类算法的若干进展-Semi supervised clustering is a method to improve the performance of clustering algorithm by using a small amount of labeled data,Some advances about semi supervised clustering algorithms are reviewed in thi
FM algorithm
- 因子分解机( FM)算法是一种基于矩阵分解的机器学习算法，是一种常用的推荐算法。(Factorization algorithm is a matrix-based machine learning algorithm, which is a commonly used recommendation algorithm.)