通过隶属度函数确定的加权KNN-BP神经网络方法,建立PM_(2.5)浓度动态实时预测模型,以PM_(2.5)、PM_(10)、NO_2、CO、O_3、SO_2等6种污染物前1 h的浓度及天气现象、温度、气压、湿度、风速、风向等6种气象条件,以及预测时刻所在一周中天...通过隶属度函数确定的加权KNN-BP神经网络方法,建立PM_(2.5)浓度动态实时预测模型,以PM_(2.5)、PM_(10)、NO_2、CO、O_3、SO_2等6种污染物前1 h的浓度及天气现象、温度、气压、湿度、风速、风向等6种气象条件,以及预测时刻所在一周中天数和该时刻所在一天当中的小时数为KNN实例的维度,选取3个近邻,根据得到的欧氏距离确定每个近邻变量的隶属度权重,最终将所有近邻的维度作为BP神经网络的输入层数据,输出要预测的下1 h PM_(2.5)浓度,该方法避免了传统BP神经网络方法不能体现历史时间窗内的数据对当前预测影响的问题。对北京市东城区监测站2014-05-01T00:00—2014-09-10T23:00的数据进行预测试验,结果表明,加权KNN-BP神经网络预测模型相较其他方法的预测误差最低,且稳定性效果最好,是PM_(2.5)浓度实时预测的有效方法。展开更多
为了有效地发现复杂网络中的重叠社区结构,引入了密度峰值聚类算法,但将此算法应用于社区发现还存在如何度量节点间距离、如何产生重叠划分结果等问题。为此提出了一种基于节点局部相似性的两阶段密度峰值重叠社区发现方法(Node Local S...为了有效地发现复杂网络中的重叠社区结构,引入了密度峰值聚类算法,但将此算法应用于社区发现还存在如何度量节点间距离、如何产生重叠划分结果等问题。为此提出了一种基于节点局部相似性的两阶段密度峰值重叠社区发现方法(Node Local Similarity Based Two-stage Density Peaks Algorithm for Overlapping Community Detection,LSDPC)。该方法结合大度节点有利指标和连接贡献度定义了一种新的节点局部相似性指标,首先通过节点局部相似性度量节点距离;然后通过节点的局部密度和最小距离计算节点中心值,利用切比雪夫不等式筛选出社区中心节点;最后经过初次划分与重叠划分两阶段得到最终的重叠社区划分结果。在真实网络数据集与合成网络数据集上的实验结果表明,所提算法可以有效发现重叠社区结构,且结果优于其他对比算法。展开更多
Collaborative filtering （CF） is one of the most popular techniques behind the success of recommendation system. It predicts the interest of users by collecting information from past users who have the same opinions....Collaborative filtering （CF） is one of the most popular techniques behind the success of recommendation system. It predicts the interest of users by collecting information from past users who have the same opinions. The most popular approaches used in CF research area are Matrix factorization methods such as SVD. However, many well- known recommendation systems do not use this method but still stick with Neighborhood models because of simplicity and explainability. There are some concerns that limit neighborhood models to achieve higher prediction accuracy. To address these concerns, we propose a new exponential fuzzy clustering （XFCM） algorithm by reformulating the clustering＇s objective function with an exponential equation in order to improve the method for membership assignment. The proposed method assigns data to the clusters by aggressively excluding irrelevant data, which is better than other fuzzy C-means （FCM） variants. The experiments show that XFCM-based CF improved 6.9% over item-based method and 3.0% over SVD in terms of mean absolute error for 100 K and 1 M MovieLens dataset.展开更多
文摘通过隶属度函数确定的加权KNN-BP神经网络方法,建立PM_(2.5)浓度动态实时预测模型,以PM_(2.5)、PM_(10)、NO_2、CO、O_3、SO_2等6种污染物前1 h的浓度及天气现象、温度、气压、湿度、风速、风向等6种气象条件,以及预测时刻所在一周中天数和该时刻所在一天当中的小时数为KNN实例的维度,选取3个近邻,根据得到的欧氏距离确定每个近邻变量的隶属度权重,最终将所有近邻的维度作为BP神经网络的输入层数据,输出要预测的下1 h PM_(2.5)浓度,该方法避免了传统BP神经网络方法不能体现历史时间窗内的数据对当前预测影响的问题。对北京市东城区监测站2014-05-01T00:00—2014-09-10T23:00的数据进行预测试验,结果表明,加权KNN-BP神经网络预测模型相较其他方法的预测误差最低,且稳定性效果最好,是PM_(2.5)浓度实时预测的有效方法。
文摘为了有效地发现复杂网络中的重叠社区结构,引入了密度峰值聚类算法,但将此算法应用于社区发现还存在如何度量节点间距离、如何产生重叠划分结果等问题。为此提出了一种基于节点局部相似性的两阶段密度峰值重叠社区发现方法(Node Local Similarity Based Two-stage Density Peaks Algorithm for Overlapping Community Detection,LSDPC)。该方法结合大度节点有利指标和连接贡献度定义了一种新的节点局部相似性指标,首先通过节点局部相似性度量节点距离;然后通过节点的局部密度和最小距离计算节点中心值,利用切比雪夫不等式筛选出社区中心节点;最后经过初次划分与重叠划分两阶段得到最终的重叠社区划分结果。在真实网络数据集与合成网络数据集上的实验结果表明,所提算法可以有效发现重叠社区结构,且结果优于其他对比算法。
文摘Collaborative filtering （CF） is one of the most popular techniques behind the success of recommendation system. It predicts the interest of users by collecting information from past users who have the same opinions. The most popular approaches used in CF research area are Matrix factorization methods such as SVD. However, many well- known recommendation systems do not use this method but still stick with Neighborhood models because of simplicity and explainability. There are some concerns that limit neighborhood models to achieve higher prediction accuracy. To address these concerns, we propose a new exponential fuzzy clustering （XFCM） algorithm by reformulating the clustering＇s objective function with an exponential equation in order to improve the method for membership assignment. The proposed method assigns data to the clusters by aggressively excluding irrelevant data, which is better than other fuzzy C-means （FCM） variants. The experiments show that XFCM-based CF improved 6.9% over item-based method and 3.0% over SVD in terms of mean absolute error for 100 K and 1 M MovieLens dataset.
