Data mining and multiple correspondence analysis via polynomial transformations

In the framework of the Total Quality Management, earlier studies have suggested that enterprises could harness the predictive power of Learning Management System data to develop reporting tools that identify at-risk customers/consumers and allow for more timely interventions. To support decision making in customer-centric planning tasks, exploratory multivariate data analysis is an important part of corporate data mining. To monitor the overall (dis)satisfaction with respect to the service aspects, among different exploratory tools, we focus on Multiple Correspondence Analysis via polynomial transformations to deal with ordered categorical variables and nominal ones too.