The ability of managers to analyze large volumes of data is not enough to identify all relevant associations and necessary for the decision-making process. Make use of a classification model and clustering model can generate information that typically a manager could not create without the utilization of this technology. The aim of this work is to reach a classification model linking them to clusters, based on data from purchases made by customers through electronic media in an automated manner. Precisely this model presents a set of rules to assist in decision-making applicable to a sale of vehicles, parts and accessories. For the construction of this model, we applied a process of knowledge discovery in databases. In which classification techniques and clustering techniques was evaluated in an experiment regarding accuracy, interpretability and learned a model of the computing performance. Data mining has been used to find this classifier.
Campos, V., Bueno, C., Brancher, J., Matsunaga, F., & Negrao, R. (2015). Knowledge Discovery Using an Integration of Clustering and Classification to Support Decision-making in E-commerce. Advances in Economics and Business, 3(8), 329–336. https://doi.org/10.13189/aeb.2015.030805