Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Visual Explorations in Finance
Visual Explorations in Finance
Self-Organizing Maps
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
A study for control of client value using cluster analysis
Journal of Network and Computer Applications
Neural Networks for Applied Sciences and Engineering
Neural Networks for Applied Sciences and Engineering
Inducing a marketing strategy for a new pet insurance company using decision trees
Expert Systems with Applications: An International Journal
Segmenting online game customers - The perspective of experiential marketing
Expert Systems with Applications: An International Journal
Data Mining with Decision Trees: Theroy and Applications
Data Mining with Decision Trees: Theroy and Applications
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Customer portfolio analysis using the SOM
International Journal of Business Information Systems
Electricity consumption time series profiling: a data mining application in energy industry
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
Ranking and selection of unsupervised learning marketing segmentation
Knowledge-Based Systems
| Hi-index | 0.00 |
Leveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies in this information age. Data mining technologies extract hidden information and knowledge from large data stored in databases or data warehouses, thereby supporting the corporate decision making process. In this study, we apply a two-level approach that combines SOM-Ward clustering and decision trees to conduct customer portfolio analysis for a case company. The created two-level model was then used to identify potential high-value customers from the customer base. It was found that this hybrid approach could provide more detailed and accurate information about the customer base for tailoring actionable marketing strategies.