Abstract
The analysis of customer satisfaction datasets has shown that product-related features fall into three categories (i.e., basic, performance, and excitement), which affect overall satisfaction differently. Because the relationship between product features and customer satisfaction is characterized by non-linearity and asymmetry, feature values are studied to understand the characteristics of a feature. However, existing methods are computationally expensive and work for ordinal features only. We propose a rule-based method that can be used to analyze data features regarding various characteristics of customer satisfaction. The inputs for these rules are derived by using a probabilistic feature-selection technique. In this feature selection method, mutual associations between feature values and class decisions in a pre-classified database are computed to measure the significance of feature values. The proposed method can be used for both types of features: ordinal and categorical. The proposed method is more computationally efficient than previously recommended methods. We performed experiments on a synthetic dataset with known characteristics, and our method correctly predicted the characteristics of the dataset. We also performed experiments with a real-housing dataset. The knowledge extracted from the dataset by using this method is in agreement with the domain knowledge. (C) 2012 Elsevier Inc. All rights reserved.