FDP聚类算法

说明：  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)

说明：  聚类评价指标的各种说明，非常详细，请仔细阅读。(Cluster evaluation indicators of various descriptions, very detailed.)

说明：  内附源数据、代码及word。代码包括：平稳性检验、协整检验、滞后阶数的确定、VAR 模型的拟合、脉冲响应分析、VAR 模型的预测(Stationarity test, co integration test, determination of lag order, VAR model fitting, impulse response analysis, VAR model prediction)

说明：  Python数据预处理示例，包括数据清洗、数据整合、数据变换等操作。(Python data preprocessing examples, including data cleaning, data integration, data transformation and other operations.)

主要针对行为识别，常用数据处理方法及分类，可视化 包含数据集(Mainly for behavior recognition, commonly used data processing methods and classification, visualization Contain data sets)

基于人工蚁群的的改进Kmeans,进行了改进，效果较好(Based on the improvement of artificial ant colony, Kmeans have been improved and the effect is better)

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)

说明：  用来作分类识别的KNN算法,非常好用非常好用(A very useful nearest neighbor algorithm)

说明：  用matlab 实现了kmeans算法还附有评价指标计算(Matlab to achieve kmeans algorithm also attached to the evaluation index calculation)

说明：  从数据库获取车辆在一段时间内的所有行驶记录的相关数据，确定所需数据为GPS经纬度坐标和驾驶时长等，QB模型采用MDF的思想,其基本思想为:通过平均直接翻转距离函数定义两条轨迹之间的距离，两条轨迹需要具有相同的经纬度点数，具有相同点数的轨迹最大的优点是对轨迹距离成对计算，且相同轨迹之间具有更高的分辨率，对于轨迹聚类的结果有一定的优化。(Retrieved from the database cars all over a period of time, record the related data, determine the required data for the GPS latitude and longitude coordinates, and the driving time, QB model by adopting the idea of MDF, its basic idea is: flip directly by the average distance function definition of the distance between two trajectories, two tracks will have the same latitude and longitude points, and has the biggest advantages of the same points of trajectory track distance calculation in pairs, and has higher resolution, between the same trajectory for trajectory clustering results have certain optimization.)