arima

数据挖掘算法，高效用项集挖掘算法，加权频繁项集挖掘，HUIMINER算法，源码中有详细注释

说明：  多种基本分类训练，包括支持向量机，偏最小二乘，主成分分析和线性分析(A variety of basic classification training, including support vector machine, partial least squares, principal component analysis and linear analysis)

利用一维卷积神经网络将叶子进行分类，里面包含的有数据(Classification of leaves using one dimensional convolution neural network)

一种神经网络算法：极限学习机（ELM)，包括分类和回归，仿真验证无误，适合初学者练习(A data mining algorithm: limit learning machine (ELM), including classification and regression, simulation verification is unmistakable, suitable for beginners to practice)

动画演示多种排序算法,包括冒泡排序,选择排序,插入排序,快速排序等,()

说明：  粗糙集在进行属性约简时需要求其正域，此为求正域程序(Rough Set for Positive Domain)

说明：  数据挖掘中的binning算法，用于数据预处理(Binning algorithm in data mining for data preprocessing)

一个用python实现的PCA算法，并且给了简易素材(A PCA algorithm implemented in python, and gave a simple material)

求解矩阵的最大最小特征值及对应的特征向量,以及计算条件数()

说明：  用于轴承大数据的故障诊断和数据挖掘，可将轴承的振动信息进行数组分析，获得预测模型，准确率较高(It can be used for fault diagnosis and data mining of bearing big data. It can analyze the vibration information of bearing by array and obtain the prediction model with high accuracy)