Journal of the Korean Operations Research and Management Science Society (한국경영과학회지)
- Volume 18 Issue 2
- /
- Pages.183-203
- /
- 1993
- /
- 1225-1119(pISSN)
- /
- 2733-4759(eISSN)
Splitting Algorithm Using Total Information Gain for a Market Segmentation Problem
- Kim, Jae-Kyeong (Dept. of industrial Engineering, Taejun National University of Technology) ;
- Kim, Chang-Kwon (Software Development Division Consulting Team, Samsung Data Systems) ;
- Kim, Soung-Hie (Dept. of Management Information Systems, KAIST)
- Published : 1993.08.01
Abstract
One of the most difficult and time-consuming stages in the development of the knowledge-based system is a knowledge acquisition. A splitting algorithm is developed to infer a rule-tree which can be converted to a rule-typed knowledge. A market segmentation may be performed in order to establish market strategy suitable to each market segment. As the sales data of a product market is probabilistic and noisy, it becomes necessary to prune the rule-tree-at an acceptable level while generating a rule-tree. A splitting algorithm is developed using the pruning measure based on a total amount of information gain and the measure of existing algorithms. A user can easily adjust the size of the resulting rule-tree according to his(her) preferences and problem domains. The algorithm is applied to a market segmentation problem of a medium-large computer market. The algorithm is illustrated step by step with a sales data of a computer market and is analyzed.
Keywords