Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Estimating campaign benefits and modeling lift
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Selecting features in microarray classification using ROC curves
Pattern Recognition
Response modeling with support vector regression
Expert Systems with Applications: An International Journal
Using a hybrid meta-evolutionary rule mining approach as a classification response model
Expert Systems with Applications: An International Journal
Handling class imbalance in customer churn prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Response modeling with support vector machines
Expert Systems with Applications: An International Journal
Constructing response model using ensemble based on feature subset selection
Expert Systems with Applications: An International Journal
Adjusting and generalizing CBA algorithm to handling class imbalance
Expert Systems with Applications: An International Journal
Including spatial interdependence in customer acquisition models: A cross-category comparison
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Direct marketing is a common data mining application. Previous studies largely adopt the approach that, as the subjects for response model predictions, the entire customer population or filtered through certain attribute values is based on recommendations made from sales professionals. However, such methods may reduce response rates due to an oversized potential customer population, thus diminishing the accuracy of the prediction model. In resolve this problem, this work presents proposes a novel forecasting method that integrates the union sequential pattern with classification algorithms to facilitate the construction of customer response models. Based on use of a union sequential pattern, the potential customer size is established by identifying attributes with a high level of association. The prediction model is then constructed using classification algorithms such as support vector machines and logistic regression. Consequently, the problem involving the setting of range for potential customers can be solved, as well as the time spent on processing extended lists of customers during prediction. Finally, predicted potential Internet-phone customers and churning mobile-phone customers of a telecommunication company in Taiwan as are taken as an illustrative example, based on the proposed prediction model. The proposed method more accurately predicts potential customers than those of previous studies.