Classifiers___Bagging+Knn

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

通过优化的k-means算法 采用了密度和优化评测函数实现了对Iris等数据集的聚类。 

holt-winters 三次指数平滑算法,时间序列预测算法，带测试数据及Demo(Triple Order Exponential Smoothing, Holt-Winters algorithm, time series prediction algorithm with test data and Demo)

说明：  波形设计算法，阵列信号处理等相关知识的介绍仿真等(Introduction and Simulation of waveform design algorithm, array signal processing and other related knowledge)

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：  简单的LSTM进行预测，附带数据集方便测试(simple test of LSTM is used for prediction , and related datasets is attached in the file.)

Eclat算法是一种深度优先算法,采用垂直数据表示形式,在概念格理论的基础上利用基于前缀的等价关系将搜索空间(概念格)划分为较小的子空间(子概念格)。Eclat算法采用方法二计算支持度。对候选k项集进行支持度计算时,不需再次扫描数据库,仅在一次扫描数据库后得到每个1项集的支持度,而候选k项集的支持度就是在对k-1项集进行交集操作后得到的该k项集Tidset中元素的个数。本算法利用diffset数据格式实现。

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.)

Matrix QR decomposition of digital analysis of the more difficult procedures, it is worth a look

用遗传算法优化支持向量回归机C、g、p参数(Optimization of C, G, P parameters of support vector regression machine by genetic algorithm)