Data mining (DM) is a new emerging discipline that aims to extract knowledge from data using several techniques. DM turned out to be useful in business where the data describing the customers and their transactions is in the order of terabytes. In this paper, we propose an approach for building customer models (said also profiles in the literature) from business data. Our approach is three-step. In the first step, we use fuzzy clustering to categorize customers, i.e., determine groups of customers. A key feature is that the number of groups (or clusters) is computed automatically from data using the partition entropy as a validity criteria. In the second step, we proceed to a dimensionality reduction which aims at keeping for each group of customers only the most informative attributes. For this, we define the information loss to quantify the information degree of an attribute. Hence, and as a result to this second step, we obtain groups of customers each described by a distinct set of attributes. In the third and final step, we use backpropagation neural networks to extract useful knowledge from these groups. Experimental results on real-world data sets reveal a good performance of our approach and should simulate future research.
Analysis of regional development disparities in Ukraine with fuzzy clustering technique