资源列表
JBIRCH-src
- 本代码是关于数据挖掘的层次聚类算法的JAVA编程实例，请勿用于商业用途上-The code is JAVA programming examples on data mining hierarchical clustering algorithm, not for commercial use
BasicELM
- 用一个简短的例子说明了随机权神经网络算法，算是对深度学习的入门的一个小的巩固-With a brief example illustrates the random weights neural network algorithm, considered for entry-depth study of a small consolidation
apriori
- Apriori algorithm是关联规则里一项基本算法。是由Rakesh Agrawal和Ramakrishnan Srikant两位博士在1994年提出的关联规则挖掘算法。关联规则的目的就是在一个数据集中找出项与项之间的关系。 文件中为实现Apriori算法的matlab函数-Apriori algorithm in association rules is a basic algorithm. By Rakesh Agrawal and Ramakrishnan Srikant Dr
community
- 基于FastUnfolding实现的社区发现方法，网上找的源码。-FastUnfolding
heston-summer-xls
- GAUSS has two electronic help systems, corresponding to the GAUSS pdf manuals (available at http://www.aptech.com). 1. The Command Reference is an easy way to pick up information on commands (as long as they are not deemed obsolete ), and is or
Achieve-mixing-MATLAB-and-VB
- 实现MATLAB与VB的混合，是二者的嵌套结合，对于VB镶嵌在MATLAB中是很好地实例展示-MATLAB realize mixed with VB, the combination of the two nested for VB is embedded in MATLAB good examples show
GUI-toolbar-and-tips
- GUI工具栏及其使用技巧，这里给了两个具体实例和其中一个画图过程。-GUI toolbar and tips, here to give two concrete examples and one drawing.
roughset-into-weka
- 可以嵌入weka中的粗糙集约简算法，进一步扩充weka的数据挖掘功能-Weka can be embedded in the rough set reduction algorithm to further expand weka data mining functions
Apriori
- 数据挖掘中的经典算法apriori。输入项集和最小支持度，输出频繁项集。-Data Mining the classical algorithm apriori. Entry and set minimum support, output frequent item sets.
EM
- 对于混合高斯分布的情况，使用最大期望算法，通过不断计算每个样本的均值与方差，使得似然函数达到最大值。可以很好地处理满足一定概率分布的数据。 代码中通过mvnrnd()函数，设定其中的参数，产生符合混合高斯分布的一组数据集。-For the case of a mixed Gaussian distribution, using expectation-maximization algorithm, through continuous calculation of the mean and
multiverso-master
- Multiverso is a parameter server based framework for training machine learning models on big data with numbers of machines. It is currently a standard C++ library and provides a series of friendly programming interfaces. With such easy-to-use APIs, m
lightlda-master
- LightLDA is a distributed system for large scale topic modeling. It implements a distributed sampler that enables very large data sizes and models. LightLDA improves sampling throughput and convergence speed via a fast O(1) metropolis-Hastings algori