ROCK: a robust clustering algorithm for categorical attributes
Information Systems
Validation indices for graph clustering
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Clustering data with measurement errors
Computational Statistics & Data Analysis
A survey of kernel and spectral methods for clustering
Pattern Recognition
Optimizing back-propagation networks via a calibrated heuristic algorithm with an orthogonal array
Expert Systems with Applications: An International Journal
Comparison of clustering algorithms for analog modulation classification
Expert Systems with Applications: An International Journal
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Segmenting and mining the ERP users' perceived benefits using the rough set approach
Expert Systems with Applications: An International Journal
Segmenting customers by transaction data with concept hierarchy
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Customer segmentation is a key element for target marketing or market segmentation. Although there are quite a lot of ways available for segmentation today, most of them emphasize numeric calculation instead of commercial goals. In this study, we propose an improved segmentation method called transaction pattern based customer segmentation with neural network (TPCSNN) based on customer's historical transaction patterns. First of all, it filters transaction data from database for records with typical patterns. Next, it reduces inter-group correlation coefficient and increases inner cluster density to achieve customer segmentation by iterative calculation. Then, it utilizes neural network to dig patterns of consumptive behaviors. The results can be used to segment new customers. By this way, customer segmentation can be implemented in very short time and costs little. Furthermore, the results of segmentation are also analyzed and explained in this study.