Knowledge Extraction from Survey Data Using Neural Networks

Surveys are an important tool for researchers. Survey attributes are typically discrete data measured on a Likert scale. Collected responses from the survey contain an enormous amount of data. It is increasingly important to develop powerful means for clustering such data and knowledge extraction that could help in decision-making. The process of clustering becomes complex if the number of survey attributes is large. Another major issue in Likert-Scale data is the uniqueness of tuples. A large number of unique tuples may result in a large number of patterns and that may increase the complexity of the knowledge extraction process. Also, the outcome from the knowledge extraction process may not be satisfactory. The main focus of this research is to propose a method to solve the clustering problem of Likert-scale survey data and to propose an efficient knowledge extraction methodology that can work even if the number of unique patterns is large. The proposed method uses an unsupervised neural network for clustering, and an extended version of the conjunctive rule extraction algorithm has been vii proposed to extract knowledge in the form of rules. In order to verify the effectiveness of the proposed method, it is applied to two sets of Likert scale survey data, and results show that the proposed method produces rule sets that are comprehensive and concise without affecting the accuracy of the classifier.