The term “customer churn” is used in the industry of information and communication technology (ICT) to indicate those customers who are about to leave for a new competitor, or end their subscription. Predicting this...The term “customer churn” is used in the industry of information and communication technology (ICT) to indicate those customers who are about to leave for a new competitor, or end their subscription. Predicting this behavior is very important for real life market and competition, and it is essential to manage it. In this paper, three hybrid models are investigated to develop an accurate and efficient churn prediction model. The three models are based on two phases;the clustering phase and the prediction phase. In the first phase, customer data is filtered. The second phase predicts the customer behavior. The first model investigates the k-means algorithm for data filtering, and Multilayer Perceptron Artificial Neural Networks (MLP-ANN) for prediction. The second model uses hierarchical clustering with MLP-ANN. The third one uses self organizing maps (SOM) with MLP-ANN. The three models are developed based on real data then the accuracy and churn rate values are calculated and compared. The comparison with the other models shows that the three hybrid models outperformed single common models.展开更多
大量GIS应用的地图显示功能具有多尺度特性 .Hilbert R Tree索引与此特性不相适应 .使用它访问绘图数据存在I/O粒度偏小和同等级簇聚程度低两个问题 ,导致访问效率不高 .该文对它作出改进 ,提出一种新型索引即多级Hilbert R Tree索引 (H...大量GIS应用的地图显示功能具有多尺度特性 .Hilbert R Tree索引与此特性不相适应 .使用它访问绘图数据存在I/O粒度偏小和同等级簇聚程度低两个问题 ,导致访问效率不高 .该文对它作出改进 ,提出一种新型索引即多级Hilbert R Tree索引 (HierarchicalHilbert R Tree ,HHRT) .HHRT解决了上述两个问题 .实验证明后者的访问效率比前者有较大的提高 .展开更多
文摘The term “customer churn” is used in the industry of information and communication technology (ICT) to indicate those customers who are about to leave for a new competitor, or end their subscription. Predicting this behavior is very important for real life market and competition, and it is essential to manage it. In this paper, three hybrid models are investigated to develop an accurate and efficient churn prediction model. The three models are based on two phases;the clustering phase and the prediction phase. In the first phase, customer data is filtered. The second phase predicts the customer behavior. The first model investigates the k-means algorithm for data filtering, and Multilayer Perceptron Artificial Neural Networks (MLP-ANN) for prediction. The second model uses hierarchical clustering with MLP-ANN. The third one uses self organizing maps (SOM) with MLP-ANN. The three models are developed based on real data then the accuracy and churn rate values are calculated and compared. The comparison with the other models shows that the three hybrid models outperformed single common models.
文摘大量GIS应用的地图显示功能具有多尺度特性 .Hilbert R Tree索引与此特性不相适应 .使用它访问绘图数据存在I/O粒度偏小和同等级簇聚程度低两个问题 ,导致访问效率不高 .该文对它作出改进 ,提出一种新型索引即多级Hilbert R Tree索引 (HierarchicalHilbert R Tree ,HHRT) .HHRT解决了上述两个问题 .实验证明后者的访问效率比前者有较大的提高 .
