搜索资源列表
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_clique
- [X,Y,Z] = k_clique(k,A) Inputs: k - clique size A - adjacency matrix Outputs: X - detected communities Y - all cliques (i.e. complete subgraphs that are not parts of larger complete subgraphs) Z - k-clique matrix-k-clique alg
gplvm
- 这是一个用于高斯过程隐变量模型的工具箱，其中包含了MATLAB/C/PYTHON三种语言版本-As of July 2005 a C++ implementation of the GPLVM exists which has most of the flexibility of this software but runs much faster. However as of this time it cannot handle very large data sets as the spar
DBScan03
- DBScan算法实现，用Java高级编程语言正确实现DBSCAN算法，DBScan是一种基于密度的聚类算法，它有一个核心点的概念：如果一个点，在距它e的范围内有不少于MinP个点，则该点就是核心点。核心和它e范围内的邻居形成一个簇。在一个簇内如果出现多个点都是核心点，则以这些核心点为中心的簇要合并。最终输出找到的簇及其数据点。-DBScan algorithm, using high-level programming language Java is implemented correctly
Newman
- 数据挖掘 社团挖掘 NewMan算法 参考文献：M. E. J. Newman,Fast algorithm for detecting community structure in networks -data Mining, Newman
machineLearning_R
- 机器学习入门很好的代码，以电子商务案例进行实践，使用R语言实现-Getting good code machine learning, e-commerce case to practice using the R language
E-Algorithm
- 用于数据挖掘分类的算法，E-Alothm，并附有10个左右的KEEL专用数据集，算法实现+实际例程。-For the data mining classification algorithm, E-Alothm, and with 10 or so KEEL dedicated data set, the algorithm to achieve+ practical routines.
Naive-Bayes
- Python实现朴素贝叶斯分类，即Naive Bayes Classifier（NB），数据集为pima-indians印第安人糖尿病数据集。-Python implementation naive Bayes classifier, i.e. Naive Bayes Classifier (NB), the data set is pima-indians Indians diabetes data sets.
spss数据挖掘
- 该资料包含经典电子书籍spss数据挖掘方法的影印版(This material contains a photocopy of the classic e-book spss data mining method.)