SSTCA

1基于MapReduce的K-Means聚类算法， 2基于MapReduce的分类算法 3MapReduce的频繁项集挖掘算法(1 K-Means clustering algorithm based on MapReduce, 2 classification algorithm based on MapReduce 3 Mining Algorithm for frequent itemsets based on MapReduce)

说明：  python PCA算法原理推导，及源代码实现，并基于PCA算法实现图片压缩，附pdf文件说明(Python PCA algorithm principle derivation, and source code implementation, and based on PCA algorithm to achieve image compression, with PDF file description)

朴素贝叶斯分类的分类器实现，使用的是matlab语言。内含测试集和训练集，可直接运行，readme.txt文件中说明了数据格式

说明：  隐马尔科夫实现，包含forward-hmm, Viterbi-hmm, Baum-Welch-hmm(Hidden Markov implementation, including forward-hmm, Viterbi-hmm, Baum-Welch-hmm)

此为利用optics聚类方法剔除风电异常数据后，采用极限学习机验证的代码(optics data mining)

FDTD three-dimensional CPML

通过贝叶斯信息准则确定高斯混合聚类方法的聚类簇数(Determining the Cluster Number of GMM Clusters by BIC)

用于信号特征提取，提取信号的小波包能量谱(Extracting the wavelet packet energy spectrum of the signal)

数据挖掘经典算法，频繁项集挖掘经典算法，CFPGROWTH算法，JAVA实现，代码中有详细注释

EMD经验模态分解算法程序，适合非线性，非平稳时间序列的处理(EMD empirical mode decomposition algorithm program)