WDMAP6

基于混合信号的小波包分解技术在故障特征提取中的应用(Feature Extraction Using Multisignal Wavelet Packet Decomposition)

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。

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)

bp网络实现认知无线电的检测和预测,从而达到了对频谱的分配(Bp network realizes the detection and prediction of cognitive radio, thus achieving the spectrum allocation.)

应用python编写的百度指数新闻爬取代码(baiduindex spider)

利用urllib.request进行爬虫， 下载百度视频页面的所有图片保存到本地(Use urllib.request for crawl. Download all the pictures from Baidu video page to local.)

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)

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：  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.)

将数据集转换为拉普拉斯矩阵，然后利用基于图论的谱聚类进行聚类。拉普拉斯矩阵采用高斯核函数，全连接方法计算。谱聚类擅长处理高维数据或非凸数据集。(The data set is transformed into Laplacian matrix, and then clustered by spectral clustering based on graph theory. The Laplacian matrix is calculated by using the Gauss kernel function and the full connection method. Spectral clustering is good at dealing with high-dimensional or non-convex data sets.)