Fuzzy Clustering-based GMDH Model to Feature Selection in Customer Analysis

Feature selection has recently been the subject of intensive research in data mining, especially for datasets with a large number of descriptive attributes such as feature selection in customer relationship management (CRM). In this paper, FRI algorithm which has some deficiencies in feature selection of market segments groups is improved. A new FC-based GMDH model is built. It has the advantage of combining both qualitative and quantitative information in the decision analysis, which is extremely important for CRM. To derive the decision rules from different customer group for identifying features that contribute to CRM, both fuzzy clustering and heuristic algorithm are developed in this paper. It has been demonstrated in the empirical research that the proposed algorithm is able to derive the rules and identify the most significant features more accuracy than FRI when feature difference between customer segments is not obvious, which is unique and useful in solving CRM problems. The results showed the practical viability of the proposed approach for customer feature selection.