Applying case-based reasoning: techniques for enterprise systems
Applying case-based reasoning: techniques for enterprise systems
An investigation of machine learning based prediction systems
Journal of Systems and Software - Special issue on empirical studies of software development and evolution
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Expert Systems with Applications: An International Journal
Two stages of case-based reasoning - Integrating genetic algorithm with data mining mechanism
Expert Systems with Applications: An International Journal
When Similar Problems Don't Have Similar Solutions
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
A Methodology for Analyzing Case Retrieval from a Clustered Case Memory
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Retrieval Based on Self-explicative Memories
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Recognizing yield patterns through hybrid applications of machine learning techniques
Information Sciences: an International Journal
A temporal case retrieval model to predict railway passenger arrivals
Expert Systems with Applications: An International Journal
Explanation of a Clustered Case Memory Organization
Proceedings of the 2007 conference on Artificial Intelligence Research and Development
Integration of a Methodology for Cluster-Based Retrieval in jColibri
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Evolving fuzzy case-based reasoning in wholesaler's returning book forecasting
Proceedings of the 2009 International Conference on Hybrid Information Technology
Expert Systems with Applications: An International Journal
An efficient greedy K-means algorithm for global gene trajectory clustering
Expert Systems with Applications: An International Journal
Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry
Expert Systems with Applications: An International Journal
Combinations of case-based reasoning with other intelligent methods
International Journal of Hybrid Intelligent Systems - CIMA-08
Expert Systems with Applications: An International Journal
A sales forecasting model for new-released and nonlinear sales trend products
Expert Systems with Applications: An International Journal
Clustering Indian stock market data for portfolio management
Expert Systems with Applications: An International Journal
The development of a weighted evolving fuzzy neural network
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
A hybrid SARIMA wavelet transform method for sales forecasting
Decision Support Systems
Expert Systems with Applications: An International Journal
A recommender mechanism based on case-based reasoning
Expert Systems with Applications: An International Journal
Evolving case-based reasoning with genetic algorithm in wholesaler's returning book forecasting
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Journal of Intelligent Manufacturing
Computer Methods and Programs in Biomedicine
Hi-index | 12.07 |
In this paper, we proposed a hybrid system to combine the self-organizing map (SOM) of neural network with case-based reasoning (CBR) method, for sales forecast of new released books. CBR systems have been successfully used in several domains of artificial intelligence. In order to enhance efficiency and capability of CBR systems, we connected the SOM method to deal with cluster problems of CBR systems, SOM/CBR for short. Case base is acquired from a book selling data of a wholesaler in Taiwan, and it is applied by SOM/CBR to forecast sales of new released books. We found the SOM/CBR method has excellent performance. The result of the prediction of SOM/CBR was compared with the results of K/CBR, which is divided by K-mean, and traditional CBR. We find out that the SOM/CBR is more accurate and efficient when being applied to the forecast of the data than K/CBR or traditional CBR.