K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper propo...K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper proposes an improved K-means algorithm based on the similarity matrix. The im- proved algorithm can effectively avoid the random selection of initial center points, therefore it can provide effective initial points for clustering process, and reduce the fluctuation of clustering results which are resulted from initial points selections, thus a better clustering quality can be obtained. The experimental results also show that the F-measure of the improved K-means algorithm has been greatly improved and the clustering results are more stable.展开更多
为实现对交通流局部特征的有效提取,提高交通速度预测模型的可解释性,提出基于K-means聚类与偏最小二乘(Partial Least Squares,PLS)回归的交通速度短时预测模型。模型采用时空相关矩阵挖掘路网中相邻路段交通速度之间的关联性,利用K-me...为实现对交通流局部特征的有效提取,提高交通速度预测模型的可解释性,提出基于K-means聚类与偏最小二乘(Partial Least Squares,PLS)回归的交通速度短时预测模型。模型采用时空相关矩阵挖掘路网中相邻路段交通速度之间的关联性,利用K-means聚类算法划分历史数据集,并选取实测出租车GPS数据验证模型对交通速度短时预测的准确性。实验结果表明,与ARIMA、PLS回归和LSTM模型相比,该模型的预测误差减少了约30%。展开更多
传统矩阵分解算法和基于用户画像的算法存在数据稀疏性和冷启动等问题,且多数情况下只注重于用户项目交互数据,而对用户本身的属性信息缺少借鉴,从而导致推荐准确性不高。将K-means与矩阵分解相结合,提出了一种基于K-means的矩阵分解推...传统矩阵分解算法和基于用户画像的算法存在数据稀疏性和冷启动等问题,且多数情况下只注重于用户项目交互数据,而对用户本身的属性信息缺少借鉴,从而导致推荐准确性不高。将K-means与矩阵分解相结合,提出了一种基于K-means的矩阵分解推荐算法(Matrix Decomposition Based on K-means,KMMD)。该算法融合用户属性和用户项目交互评级数据作为输入,先将用户进行K-means聚类,得到近邻用户集,再将近邻用户-项目评级矩阵进行分解和重构,得到预测评级并排序推荐。将算法在MovieLens公开数据集上进行仿真实验,结果表明KMMD推荐算法在召回率和精确度上有了进一步的提高,并且对用户冷启动问题做出了很大的改善。展开更多
文摘K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper proposes an improved K-means algorithm based on the similarity matrix. The im- proved algorithm can effectively avoid the random selection of initial center points, therefore it can provide effective initial points for clustering process, and reduce the fluctuation of clustering results which are resulted from initial points selections, thus a better clustering quality can be obtained. The experimental results also show that the F-measure of the improved K-means algorithm has been greatly improved and the clustering results are more stable.
文摘为实现对交通流局部特征的有效提取,提高交通速度预测模型的可解释性,提出基于K-means聚类与偏最小二乘(Partial Least Squares,PLS)回归的交通速度短时预测模型。模型采用时空相关矩阵挖掘路网中相邻路段交通速度之间的关联性,利用K-means聚类算法划分历史数据集,并选取实测出租车GPS数据验证模型对交通速度短时预测的准确性。实验结果表明,与ARIMA、PLS回归和LSTM模型相比,该模型的预测误差减少了约30%。
文摘传统矩阵分解算法和基于用户画像的算法存在数据稀疏性和冷启动等问题,且多数情况下只注重于用户项目交互数据,而对用户本身的属性信息缺少借鉴,从而导致推荐准确性不高。将K-means与矩阵分解相结合,提出了一种基于K-means的矩阵分解推荐算法(Matrix Decomposition Based on K-means,KMMD)。该算法融合用户属性和用户项目交互评级数据作为输入,先将用户进行K-means聚类,得到近邻用户集,再将近邻用户-项目评级矩阵进行分解和重构,得到预测评级并排序推荐。将算法在MovieLens公开数据集上进行仿真实验,结果表明KMMD推荐算法在召回率和精确度上有了进一步的提高,并且对用户冷启动问题做出了很大的改善。
